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A Novel Wireless-Netted UWB Life-Detection Radar System for Quasi-Static Person Sensing

A Novel Wireless-Netted UWB Life-Detection Radar System for Quasi-Static Person Sensing applied sciences Article A Novel Wireless-Netted UWB Life-Detection Radar System for Quasi-Static Person Sensing 1 , 2 , 3 1 , 2 , 1 , 2 1 , 2 Kun Yan , Shiyou Wu *, Shengbo Ye and Guangyou Fang Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China; yorkstudio@foxmail.com (K.Y.); shengboye@163.com (S.Y.); gyfang@mail.ie.ac.cn (G.F.) The Key Laboratory of Electromagnetic Radiation and Sensing Technology, Chinese Academy of Science, Haidian District North 4th Ring West Road 19th, Beijing 100190, China School of Electronic, Electrical and Communication Engineering, University of the Chinese Academy of Sciences, Beijing 100039, China * Correspondence: wusy@aircas.ac.cn Abstract: In actual life-detection radar applications, a quasi-static person with weak respiration is difficult to find when relying on the echoes from a single fixed observation point. To effectively sense the weak respiration of a quasi-static person in complex through-wall and through-floor conditions, this paper proposes a novel multi-observation point detection system composed of multiple Golay complementary coded radars in which communication and synchronization are carried out wirelessly. The collaboration structure and Golay complementary coded transmitter improve the signal to noise ratio (SNR). Proof-of-principle experiments are carried out with our designed radar prototype and prove that the radar system can detect a respiring target 21 m behind a brick wall or a respiring target behind two levels of reinforced concrete floors, validating the effectiveness of a multi-observation point working mode for the efficient detection of weak human respiration. Keywords: life detection radar; multi-observation point system; through-the-wall radar; Golay complementary code Citation: Yan, K.; Wu, S.; Ye, S.; Fang, G. A Novel Wireless-Netted UWB Life-Detection Radar System for Quasi-Static Person Sensing. Appl. Sci. 1. Introduction 2021, 11, 424. https://doi.org/ In rescue applications, there is increasing demand for mechanisms to improve the 10.3390/app11010424 capability to sense trapped persons in complex scenes. Ultra-wideband (UWB) radar tech- nology provides strong anti-interference ability, high-range resolution and penetrability, Received: 9 December 2020 which plays an important role in detecting trapped persons [1–4]. It can detect the vital Accepted: 30 December 2020 signs of trapped quasi-static persons with weak micro-movements [5–8]. However, the de- Published: 4 January 2021 tection capability of single fixed observation point systems is limited in complex conditions. In order to increase the detection capabilities and detection rate of weak respiration targets Publisher’s Note: MDPI stays neu- in complex environments, novel transmit signal designs are proposed for higher signal tral with regard to jurisdictional clai- to noise ratios (SNRs) in [9–13]. The multiple input–multiple output (MIMO) technology, ms in published maps and institutio- which uses a real aperture with multiple transceiver combinations, presents an instant nal affiliations. imaging resolution and high clutter suppression capability, and it has been widely used in [14–18]. Further, the multi-view and netted radar systems were found to improve the SNR and increase rescue efficiency in [19–22]. The development of UWB radar vital sign Copyright: © 2021 by the authors. Li- detection techniques that allow multi-point observation and data association processing is censee MDPI, Basel, Switzerland. becoming a priority, motivating the design of multi-observation point detection systems This article is an open access article that increase rescue efficiency. distributed under the terms and con- In this paper, we propose a novel multi-observation point detection system composed ditions of the Creative Commons At- of multiple Golay complementary coded UWB life-detection radars. The communication tribution (CC BY) license (https:// and synchronization between radars are carried out wirelessly. For the echoes received creativecommons.org/licenses/by/ at different observation points, to improve the SNR of vital signs, the cross-correlation 4.0/). Appl. Sci. 2021, 11, 424. https://doi.org/10.3390/app11010424 https://www.mdpi.com/journal/applsci Appl. Sci. 2021, 11, x FOR PEER REVIEW 2 of 14 Appl. Sci. 2021, 11, 424 2 of 14 received at different observation points, to improve the SNR of vital signs, the cross-cor- relation operation is performed in the slow-time dimension. Meanwhile, the use of a low operation is performed in the slow-time dimension. Meanwhile, the use of a low sidelobe sidelobe sinusoidal modulation Golay complementary coded signal with a center fre- sinusoidal modulation Golay complementary coded signal with a center frequency of about quency of about 1 GHz as the transmit signal can further improve the SNR. In addition, 1 GHz as the transmit signal can further improve the SNR. In addition, the self-positioning the self-positioning technology is deployed in each UWB life-detection radar to make their technology is deployed in each UWB life-detection radar to make their positions known. positions known. Finally, three experiments are carried out involving penetrating walls Finally, three experiments are carried out involving penetrating walls and reinforced and reinforced concrete floors, verifying the detection performance of the proposed multi- concrete floors, verifying the detection performance of the proposed multi-observation observation point detection system. point detection system. 2. Multi-Observation Point Detection System 2. Multi-Observation Point Detection System As shown in Figure 1, the architecture of the proposed wireless-netted multi-obser- As shown in Figure 1, the architecture of the proposed wireless-netted multi-observation vation point detection system includes several independent single-channel Golay comple- point detection system includes several independent single-channel Golay complementary mentary coded UWB life-detection radars, which are taken as the network nodes con- coded UWB life-detection radars, which are taken as the network nodes controlled by a co tro ntll ro ed l h by ost a . T ch ont e rrol ada host rs a.r e Tr he esp rad onar sib s lare e forespon r the ge si nb ele ra f tor ionth , te ra gener nsmis at si io on n, atr nansmi d reces psi tion on and of th recept e Gola ion y co of m tp he lem Go en lay ta rcom y coplemen ded sign tary al. T coded he det sa ig iln ed al. dTh esie gn deta of t il h ee d sdesign ystem io s f dth isc e u sys ssetem d in is Se dis ctic ouss n 3e . d Th in e Section control h 3. oTh st ie s con resp tro onls host ible fio s rrtespon he hus m ibl an e –fo mr ath che in hum e intan erf– am ce achi , col n la e b io nt ra erfa tive ce, ac coll quiabora sitiontive andacquisition data proce an ssid ng d.ata Bypro usice ng ssi tim ng. e d By iv u issi io n n g m tim ule ti p dl ivi exsi in o g n (mu TDM ltiplex ) and intg he (TDM) star topology, the control host coordinates these network nodes during the multiple observation and the star topology, the control host coordinates these network nodes during the mul- point detection process to prevent mutual interference. tiple observation point detection process to prevent mutual interference. Figure 1. The wireless-netted multi-observation point detection architecture. Figure 1. The wireless-netted multi-observation point detection architecture. 3. Design of Golay Complementary Coded Radar 3. Design of Golay Complementary Coded Radar The block diagram of the Golay complementary coded radar for a multi-observation The block diagram of the Golay complementary coded radar for a multi-observation point detection system is shown in Figure 2. There are six key components: a digital point detection system is shown in Figure 2. There are six key components: a digital trans- transmitter, a dual-channel receiver, a clock manager, a network clock, a self-positioning mitter, a dual-channel receiver, a clock manager, a network clock, a self-positioning mod- module and a wireless communication module. A Xilinx Artix-7 Field Programmable Gate ule and a wireless communication module. A Xilinx Artix-7 Field Programmable Gate Array (FPGA) is used as the main controller unit to manage the peripherals of the radar. To Array (FPGA) is used as the main controller unit to manage the peripherals of the radar. reduce the system cost and improve the spurious-free dynamic range, the equivalent-time To reduce the system cost and improve the spurious-free dynamic range, the equivalent- sampling technique is adopted. A 16-bit analog to digital converter (ADC) with a maximal time sampling technique is adopted. A 16-bit analog to digital converter (ADC) with a sampling rate of 160 Mbps and a full power bandwidth of 1.4 GHz is used as the receiver. maximal sampling rate of 160 Mbps and a full power bandwidth of 1.4 GHz is used as the A pair of bow-tie antennas are used for electromagnetic radiation and reception. A W5300 receiver. A pair of bow-tie antennas are used for electromagnetic radiation and reception. chip is used to exchange the raw radar data and commands with the control host by the A W5300 chip is used to exchange the raw radar data and commands with the control host TCP network protocol, and the wireless bridge realizes the wireless communication. In the by the TCP network protocol, and the wireless bridge realizes the wireless communica- following, the Golay complementary coded signal transmitter (marked A in Figure 2), the tion. In the following, the Golay complementary coded signal transmitter (marked A in network clock module (marked D in Figure 2), the self-positioning module (marked E in Figure 2) and antennas are described in detail. Appl. Sci. 2021, 11, x FOR PEER REVIEW 3 of 14 Appl. Sci. 2021, 11, 424 3 of 14 Figure 2), the network clock module (marked D in Figure 2), the self-positioning module (marked E in Figure 2) and antennas are described in detail. 2GHz Signal 14bit Power LPF LNA TX Generator DAC LPF Splitter Controller PART A. The Transmitter 125MHz SPI Double Equivalent- Programmable Delay Line LPF Channel time Sampler 16bit RX LPF LNA ADC Controller PART B. The Equivalent-Time Sampler 2GHz DAC PLL+VCO Single Board 125MHz FPGA 50MHz Clock Divider(2) Divider(8) Synchronizati 125MHz Delay SPI Divider(2) Divider(8) on Controller Line PART C. The Single Board Synchronous Module CC1310 RF Net Clock SPI 433MHz Two-way Circuitry Controller Antenna Synchronization Algorithm Counter PART D. Net Clock Synchronous Module STM32 MCU DWM1000 Self- SPI Double-sided Two-way positioning SPI UWB Ranging Algorithm RF Circuitry Controller Antenna PART E. Self-positioning Module WIFI Data W5300 Wireless Net RJ45 Transmission 16bit WIFI Bridge TCP Protocol Unit Antenna PART F. Data Transmission Module Figu Figure re 2. 2. The b The block lock d diagram iagram of of the Golay the Golay c complementary omplementary co coded ded radar radar. . UWB: UWB: ultra-wideband. ultra-wideband. 3.1. The Golay Complementary Coded Signal Transmitter 3.1. The Golay Complementary Coded Signal Transmitter The selection of the transmitting signal is a key element of the radar system. The The selection of the transmitting signal is a key element of the radar system. The pseudo-random sequence has been widely applied to modulate the transmitting signal pseudo-random sequence has been widely applied to modulate the transmitting signal of of through-the-wall radar due to its high signal to noise ratio (SNR) and high range through-the-wall radar due to its high signal to noise ratio (SNR) and high range resolu- resolution [23,24]. Of these, the m-sequence, Gold sequence and Golay complementary tion [23,24]. Of these, the m-sequence, Gold sequence and Golay complementary sequence sequence are most commonly used. The m-sequence has long been applied to UWB radars are most commonly used. The m-sequence has long been applied to UWB radars due to due to its good autocorrelation characteristics. However, the number of m-sequences its good autocorrelation characteristics. However, the number of m-sequences is small, so is small, so alternative sequences of a fixed- length are limited. The Gold sequence is alternative sequences of a fixed- length are limited. The Gold sequence is based on the m- based on the m-sequence and has low autocorrelation but better cross-correlation than the sequence and has low autocorrelation but better cross-correlation than the m-sequence. m-sequence. There are a greater number of sequences, which can be used for multiple There are a greater number of sequences, which can be used for multiple input–multiple input–multiple output (MIMO) radars. The Golay complementary sequence contains two output (MIMO) radars. The Golay complementary sequence contains two sub-codes; the sub-codes; the autocorrelation functions of the two sub-codes can be added together to autocorrelation functions of the two sub-codes can be added together to eliminate the eliminate the side-lobes. The peak side-lobe ratio is better than that of the m-sequence, but side-lobes. The peak side-lobe ratio is better than that of the m-sequence, but the Golay the Golay complementary sequence’s time efficiency is low. complementary sequence’s time efficiency is low. Determining trapped human beings from raw radar echoes is extremely difficult Determining trapped human beings from raw radar echoes is extremely difficult due due to micro-motion and the low reflectivity of the human body. The autocorrelation to micro-motion and the low reflectivity of the human body. The autocorrelation charac- characteristic is the essential reference standard for selecting the pseudo random code. In teristic is the essential reference standard for selecting the pseudo random code. In com- combination with our previous research work [25], setting the Golay complementary coded bination with our previous research work [25], setting the Golay complementary coded signal as the transmitting signal can avoid the weak reflection target’s echo signal being signal as the transmitting signal can avoid the weak reflection target’s echo signal being submerged by the side-lobes due to its high peak side-lobe ratio, thus further improving submerged by the side-lobes due to its high peak side-lobe ratio, thus further improving the penetration capability and anti-noise capability of the radar. the penetration capability and anti-noise capability of the radar. Consider a binary sequence a = [a , a , a ,] , where N means the length of 0 1 N1 Consider a binary sequence 𝐚 = [𝑎 , 𝑎 , ⋯ 𝑎 , ] , where N means the length of se- 0 1 𝑁 −1 sequence a; its aperiodic autocorrelation is defined as quence 𝐚 ; its aperiodic autocorrelation is defined as N1k 𝑁 −1−𝑘 C (k) = a(j)a(j + k), 0  k  N 1. (1) a,a å ( ) ( ) 𝐶 𝑘 = ∑ 𝑎 𝑗 𝑎 (𝑗 + 𝑘 ) , 0 ≤ 𝑘 ≤ 𝑁 − 1. (1) 𝑎 ,𝑎 j=0 𝑗 =0 Artix-7 FPGA Appl. Sci. 2021, 11, 424 4 of 14 Appl. Sci. 2021, 11, x FOR PEER REVIEW 4 of 14 Consider two sequences with the length of N: a = [a , a , a ,] and b = Consider two sequences with the length of N: 𝐚 = [𝑎 , 𝑎 , ⋯ 𝑎 , ] and 𝐛 = 0 1 N1 0 1 𝑁 −1 [[b𝑏 , ,b𝑏 , ,⋯ 𝑏b ,,]] ,, 𝑎a ,, 𝑏 b ∈2 {1f , 0 1, }. 0Ifg. 𝐚 If and a and 𝐛 b sati satisfy sfy the the requirement requirement of of (2), (2), the they y can can be 00 11 𝑁N− 11 i𝑖 𝑖 i be called a Golay complementary pair. called a Golay complementary pair. 2𝑁 , 𝑘 = 0 𝐶 (𝑘 ) + 𝐶 (𝑘 ) = { 2N, k = 0 (2) 𝑎 ,𝑎 𝑏 ,𝑏 C (k) + C 0 , 𝑘 (k≠) = 0 (2) a,a b,b 0 , k 6= 0 The Golay complementary pair has good autocorrelation characteristics with no side- The Golay complementary pair has good autocorrelation characteristics with no side- lobes, and the signal peak is doubled. Any sequence included in a Golay complementary lobes, and the signal peak is doubled. Any sequence included in a Golay complementary pair is called a Golay complementary sequence [26]. The signal modulated by sequence 𝐚 pair is called a Golay complementary sequence [26]. The signal modulated by sequence a can be called Golay coded signal A, while the signal modulated by sequence 𝐛 can be can be called Golay coded signal A, while the signal modulated by sequence b can be called called Golay coded signal B. In our proposed system, a single radar only has one trans- Golay coded signal B. In our proposed system, a single radar only has one transceiver ceiver channel for electromagnetic detection; the radar uses Golay coded signal A, and channel for electromagnetic detection; the radar uses Golay coded signal A, and Golay Golay coded signal B executes electromagnetic detection in turn, and their aperiodic au- coded signal B executes electromagnetic detection in turn, and their aperiodic autocor- tocorrelation sum forms the radar A-scan. It should be noted that the high autocorrelation relation sum forms the radar A-scan. It should be noted that the high autocorrelation characteristic of the Golay complementary sequence comes at the expense of time effi- characteristic of the Golay complementary sequence comes at the expense of time efficiency, ciency, which causes a lower scan rate of the radar system than other pseudo random which causes a lower scan rate of the radar system than other pseudo random sequences. sequences. Due to the division of time slices in the TDM netted radar system, a single Due to the division of time slices in the TDM netted radar system, a single radar ’s scanning radar’s scanning rate is further reduced, which causes limited detectability for high-fre- rate is further reduced, which causes limited detectability for high-frequency movements. quency movements. The balance between the number of observation points and radar The balance between the number of observation points and radar scan rate should be scan rate should be considered during system design. In practical terms, the netted radar considered during system design. In practical terms, the netted radar system scan rate system scan rate proposed in the article is about 32 fps (see Section 4), which can detect proposed in the article is about 32 fps (see Section 4), which can detect human breathing human breathing effectively. effectively. As shown in Figure 2a, the Golay complementary coded signal transmitter consists of As shown in Figure 2a, the Golay complementary coded signal transmitter consists a controller in FPGA, a 14-bit digital to analog converter (DAC) at a 2.5 GSPS update rate, of a controller in FPGA, a 14-bit digital to analog converter (DAC) at a 2.5 GSPS update two low-pass filters, a power divider and a low-noise amplifier with 21 dB gain. In order to rate, two low-pass filters, a power divider and a low-noise amplifier with 21 dB gain. In adequately utilize the bandwidth of the antenna and emit more energy, the Golay comple- order to adequately utilize the bandwidth of the antenna and emit more energy, the Golay mentary sequence is modulated by a sinusoidal signal. Meanwhile, to balance the penetra- complementary sequence is modulated by a sinusoidal signal. Meanwhile, to balance the tion ability and the miniaturization of the antenna [27], the Golay complementary sequence penetration ability and the miniaturization of the antenna [27], the Golay complementary with ~1 ns impulse width and a center frequency of ~1 GHz is adopted here. sequence with ~1 ns impulse width and a center frequency of ~1 GHz is adopted here. For the modulation of the Golay complementary coded signal, the value “1” in the se- For the modulation of the Golay complementary coded signal, the value “1” in the quence represents a single positive impulse, while the value “0” represents a single negative sequence represents a single positive impulse, while the value “0” represents a single impulse. Instead of storing the transmitted waveform directly, both the single positive im- negative impulse. Instead of storing the transmitted waveform directly, both the single pulse and the single negative impulse are stored by bit. In this way, fewer ROMs of FPGA positive impulse and the single negative impulse are stored by bit. In this way, fewer are required. A power divider divides the filtered Golay complementary coded signal into ROMs of FPGA are required. A power divider divides the filtered Golay complementary two identical signals: one is fed to the transmitting antenna for radiation, while the other is coded signal into two identical signals: one is fed to the transmitting antenna for radiation, fed to the first channel of the receiver to obtain the reference signal for the pulse compres- while the other is fed to the first channel of the receiver to obtain the reference signal sion. The transmitted signal is shown in Figure 3a. In Figure 3b, the autocorrelation results for the pulse compression. The transmitted signal is shown in Figure 3a. In Figure 3b, of the Golay complementary coded signal A and signal B are presented, and the sidelobe of the autocorrelation results of the Golay complementary coded signal A and signal B are the sum of both is under -40 dB. presented, and the sidelobe of the sum of both is under 40 dB. Figure 3. (a) The Golay complementary coded signal sampled by the receiver. (b) The autocorrelation result of Golay code Figure 3. (a) The Golay complementary coded signal sampled by the receiver. (b) The autocorrelation result of Golay code A, Golay code B and the sum of both. A, Golay code B and the sum of both. Appl. Sci. 2021, 11, 424 5 of 14 3.2. The Network Clock Module During the multiple observation point detection process, the accurate network clock module is required to avoid mutual interference between radars which need to be activated in their own time-slices; that is, by setting a time margin and executing wireless clock synchronization, the mutual interference between radars can be prevented effectively. Here, as shown in Figure 2d, the network clock module is designed to include a SimpleLink Sub-1 GHz module based on Texas Instruments’ CC1310 chip, a counter and a network clock controller in FPGA. A counter in FPGA begins to count at an interval of 1 microsecond after power-on and plays the role of the local network clock for each radar. For different radars, the corresponding counter (called the network clock) has a different initial offset and clock drift. Assuming that the counter ’s value of the first radar joining the network is m , the counters’ values of the remaining radars can be expressed as m = m +S +T , where 1 n 1 n n n means the radar IDs, S represents the clock drift and T represents the initial offset of n n non-simultaneous start-up. m is set as the time base for all radars to realize the TDM. S can be expressed as S = p*m , where p represents the frequency tolerance of the n n radar ’s main oscillator. For the worst case, a crystal oscillator with 10 ppm frequency tolerance might introduce a 20 microsecond error within 1 s. As the human respiration detection algorithm requires 16 s of sampling echoes for one detection, the minimum time margin for S is 320 us. The measurement process of T can be regarded as wireless clock n n synchronization between radars, and all radars’ local network clocks need to be set to m after the synchronization. Here, the CC1310 SimpleLink Sub-1 Ghz module deployed on each radar is used for wireless clock synchronization. The chip provides a set of timers and taggers for radio operation. By sending and receiving the synchronization request twice, the modules can measure the differences of the local network clocks between two radars via a two-way synchronization algorithm [28], where the algorithm is similar to the Network Time Protocol. In order to avoid mutual interference between radars, the CC1310 synchronization module is activated only once for each detection. Therefore, T can be measured between the first radar (its local network clock is m ) and other radars in turn. In addition, the measurement of the CC1310 has an error of 10 us, which should be added into the time margin. In practical terms, the time margin of the network clock module is set as ~15 ms, which is longer than the above requirements (see Section 4). Thus, the clock drift and measurement error would not make the radar network clock invalid. 3.3. The Self-Positioning Module As the relative positions between radars are needed for human respiration detec- tion, the acquisition of all radars’ positions in the architecture of the proposed multiple observation point detection system cannot be neglected. Normally, the relative positions between radars are measured manually in an actual environment, which is not suitable for field applications because of manual measurement errors and the time required. To overcome this problem, a self-positioning module is deployed in each radar to sense its relative position and upload it to the control host automatically. As shown in Figure 2e, the self-positioning module is composed of a DWM1000 ultra-wideband module, an STM32 Microcontroller Unit (MCU) and a self-positioning controller in FPGA. The DWM1000, as the key component of the self-positioning module, is designed based on Decawave’s DW1000 chip. It integrates antenna, all RF circuitry, power management and clock circuitry into one module, supports four radio frequency bands from 3.5 GHz to 6.5 GHz, provides the function of timestamping and precise control of transmission times and can be used in the two-way ranging or Time Difference of Arrival (TDOA) positioning with an error within 10 cm [29–32]. Here, the two-way ranging is realized by the double-sided two-way ranging algorithm executed in the STM32 MCU to achieve the self-positioning of the radars. Although the workflows of the network clock synchronization module and self- positioning module are similar, it should be noted that the CC1310 and the DWM1000 are not interchangeable; the clock frequency of the CC1310, which serves the timestamp register, Appl. Sci. 2021, 11, x FOR PEER REVIEW 6 of 14 Appl. Sci. 2021, 11, 424 6 of 14 Although the workflows of the network clock synchronization module and self-po- sitioning module are similar, it should be noted that the CC1310 and the DWM1000 are not interchangeable; the clock frequency of the CC1310, which serves the timestamp reg- ister, is too low to provide accurate self-positioning, while the configuration and algo- is too low to provide accurate self-positioning, while the configuration and algorithm of the rithm of the DWM1000 are cumbersome and need to be controlled by an MCU, the ran- DWM1000 are cumbersome and need to be controlled by an MCU, the random response dom response time of which might cause a network clock synchronization error. time of which might cause a network clock synchronization error. 3.4. The Antennas 3.4. The Antennas The size of antennas is essential in radar design. A miniaturized radar is more The size of antennas is essential in radar design. A miniaturized radar is more adapt- adaptable to the environment. A certain compromise was made in this work between able to the environment. A certain compromise was made in this work between band- bandwidth and efficiency when designing the antenna to meet the requirements of using width and efficiency when designing the antenna to meet the requirements of using the the ultra-wideband and having a miniaturized size. Large bandwidth and high efficiency ultra-wideband and having a miniaturized size. Large bandwidth and high efficiency can can be obtained in a smaller size by using a bow-tie antenna. The top-layer patch of the be obtained in a smaller size by using a bow-tie antenna. The top-layer patch of the an- antenna has a semi-elliptical shape, meaning that the current flows through a longer path tenna has a semi-elliptical shape, meaning that the current flows through a longer path in in a small size. Simultaneously, an elliptical end is added to the bottom layer, coinciding a small size. Simultaneously, an elliptical end is added to the bottom layer, coinciding with the top layer. By loading resistors on the bottom side, the current is coupled through with the top layer. By loading resistors on the bottom side, the current is coupled through the substrate for further absorption, which improves the radiation efficiency of the antenna. the substrate for further absorption, which improves the radiation efficiency of the an- The use of a metal back cavity can also enhance the antenna’s forward radiation ability tenna. The use of a metal back cavity can also enhance the antenna’s forward radiation while further improving the isolation of the transmitting and receiving antenna and the ability while further improving the isolation of the transmitting and receiving antenna SNR of the radar system. The size of a single antenna is 140 mm  70 mm  35 mm, as and the SNR of the radar system. The size of a single antenna is 140 mm × 70 mm × 35 mm, Figure 4 shows, and the operating band is 0.5–1.5 GHz. as Figure 4 shows, and the operating band is 0.5–1.5 GHz. Figure 4. The antennas. Figure 4. The antennas. 4. 4.Radar RadarCoordination Coordination The orderly collaboration between radars depends on reasonable time-slice manage- The orderly collaboration between radars depends on reasonable time-slice manage- ment and workflow management; in this work, these were designed in accordance with ment and workflow management; in this work, these were designed in accordance with the radar parameters in Table 1. the radar parameters in Table 1. Table 1. Key parameters of the proposed radar system. ADC: analog to digital converter. Table 1. Key parameters of the proposed radar system. ADC: analog to digital converter. Property Proposed Radar Property Proposed Radar Netted radar system scan rate 32 fps Netted radar system scan rate 32 fps Center frequency of radar system 1 GHz Center frequency of radar system 1 GHz Equivalent sampling frequency (𝐹 ) 16 GSPS Equivalent sampling frequency (F ) 𝑆 16 GSPS Real-time sampling frequency 125 MSPS Real-time sampling frequency 125 MSPS Sampling points (N) 16,384 Sampling points (𝑁 ) 16384 Average times (N ) 32 Average times (𝑁 ) 32 ADC Resolution 16 bits ADC Resolution 16 bits We configured the equivalent-time sampling rate and the real-time sampling rate of the receiver to be 16 GSPS and 125 MSPS, respectively. Accordingly, the receiver was able to Appl. Sci. 2021, 11, x FOR PEER REVIEW 7 of 14 We configured the equivalent-time sampling rate and the real-time sampling rate of the receiver to be 16 GSPS and 125 MSPS, respectively. Accordingly, the receiver was able to obtain 𝑁 = 16384 sampling points for one UWB A-scan in 512 us, equaling 128 pulse repeat periods. To improve the echo’s SNR, 𝑁 = 32 UWB A-scans were required for hu- man respiration detection, and the corresponding time consumption was about 17 ms. In addition, the wireless data transmission for transmitting the radar echoes and the refer- ence signal for pulse compression with a network speed of 100 Mbps required about 6 ms. Thus, the required minimum time slice was about 23 ms. In practical terms, to retain the time margin for wireless transmission and the network clock module, each time slice was set as 32 ms. To understand the orderly collaboration between radars, the workflow timing is pre- sented in Figure 5. (1) Start Up and Netted: As the initialization stage, the host establishes the WIFI network and permits radars access. Radar IDs are distributed by the host for data acquisition, clock synchronization and self-positioning. Appl. Sci. 2021, 11, 424 7 of 14 (2) Idle: All components turn to the standby mode, waiting for the “start command”. (3) Network Clock Synchronization: The network clock synchronization is executed firstly after receiving the “start command”. obtain N = 16,384 sampling points for one UWB A-scan in 512 us, equaling 128 pulse repeat (4) Self-positioning: The relative positions between radars are measured by their self-po- periods. To improve the echo’s SNR, N = 32 UWB A-scans were required for human sitioning modules once. A respiration detection, and the corresponding time consumption was about 17 ms. In (5) Time Slice Allocation: The host verifies the validity of the radars’ topology and allo- addition, the wireless data transmission for transmitting the radar echoes and the reference cates time slices to radars. signal for pulse compression with a network speed of 100 Mbps required about 6 ms. Thus, (6) Electromagnetic Detection: According to the network clock synchronization and the the required minimum time slice was about 23 ms. In practical terms, to retain the time time slice allocation, each radar illuminates the target area and receives the echoes margin for wireless transmission and the network clock module, each time slice was set as within their time slices in turn. 32 ms. (7) Data Transmission: The echoes are uploaded to the host. To understand the orderly collaboration between radars, the workflow timing is (8) Result Output: After 16 s of sampling echoes, the host calculates the target’s position presented in Figure 5. and displays the results on the human–machine interface. Figure 5. The workflow timing. Figure 5. The workflow timing. 5. Algorithm Description for Multi-Observation Point Detection System (1) Start Up and Netted: As the initialization stage, the host establishes the WIFI network and permits radars access. Radar IDs are distributed by the host for data acquisition, Denote S(k, m) as the slow-time range matrix obtained at a single observation point, clock synchronization and self-positioning. where k = 0, 1, …, K−1 is the range cell index, and m = 0, 1, …, M−1 is the slow-time index. (2) Idle: All components turn to the standby mode, waiting for the “start command”. For quasi-static trapped human beings with quasi-periodic but weak respiration, we de- (3) Network Clock Synchronization: The network clock synchronization is executed firstly note Si(k, m) and Sj(k, m) as the output slow-time range matrixes obtained from the ith and after receiving the “start command”. jth (i ≠ j) observation points after eliminating the time-invariant clutter/interference, re- (4) Self-positioning: The relative positions between radars are measured by their self- spectively. Due to the cross-correlation function of the non-periodic noise being prone to positioning modules once. zeroing, the cross-correlation between Si(k, m) and Sj(k, m) is applied to improve the low (5) Time Slice Allocation: The host verifies the validity of the radars’ topology and allocates signal-to-noise ratio (SNR). However, the quasi-periodic component and its harmonic time slices to radars. components contained in the slow-time signal Si(k, m) and Sj(k, m) are still preserved. As- (6) Electromagnetic Detection: According to the network clock synchronization and the sume that the size of Si(k, m) and Sj(k, m) are Ku × Ma and Kv × Mb, respectively. The cross- time slice allocation, each radar illuminates the target area and receives the echoes correlation function Rij(u, v, m) is defined as within their time slices in turn. (7) Data Transmission: The echoes are uploaded to the host. (8) Result Output: After 16 s of sampling echoes, the host calculates the target’s position and displays the results on the human–machine interface. 5. Algorithm Description for Multi-Observation Point Detection System Denote S(k, m) as the slow-time range matrix obtained at a single observation point, where k = 0, 1, . . . , K1 is the range cell index, and m = 0, 1, . . . , M1 is the slow-time index. For quasi-static trapped human beings with quasi-periodic but weak respiration, we denote S (k, m) and S (k, m) as the output slow-time range matrixes obtained from the ith i j and jth (i 6= j) observation points after eliminating the time-invariant clutter/interference, respectively. Due to the cross-correlation function of the non-periodic noise being prone to zeroing, the cross-correlation between S (k, m) and S (k, m) is applied to improve the i j low signal-to-noise ratio (SNR). However, the quasi-periodic component and its harmonic components contained in the slow-time signal S (k, m) and S (k, m) are still preserved. i j Assume that the size of S (k, m) and S (k, m) are K  M and K  M , respectively. The u a v i j b cross-correlation function R (u, v, m) is defined as ij R (u, v, m) = E{S (k , m )S (k , m )} (3) ij i u a j v b Appl. Sci. 2021, 11, 424 8 of 14 AN R (u, v, m) = max {R (u, v, m)}AN {R (u, v, m)} (4) ij m ij m ij where the range cell index k 2 [0, K 1] and k 2 [0, K 1], the slow-time cell index u u v v m 2 [0, M 1], m 2 [0, M 1] and m = m – m . Then, the advance normalization a a b b a b (AN) method is applied to R (u, v, m) so that the weak quasi-periodic component of the ij quasi-static trapped human being can be further enhanced. We denote the output result of AN AN the AN method as R (u, v, m). The Fourier transform of R (u, v, m) is taken in each ij ij slow-time dimension, and the maximum corresponds to the quasi-static trapped human being; i.e., (u , v ) indicates the possible range location of the quasi-static trapped i max j max human being depending on the different ith and jth observation points. AN (u ,v ) = argmax {FFT {R (u, v, m)}}, u 6= v (5) max max uv m i j ij 6. Experiments and Results In this section, three types of experimental scenes are designed to simulate complex through-wall conditions. The corresponding experiments were carried out to verify the performance of the proposed multiple observation point detection system. Two layers of brick walls or reinforced concrete floors were penetrated to detect human respiration in type-I (see Figure 6a) and type-II (see Figure 6b) experimental scenes, respectively. For the type-III scene, the propagation distance effect in the through-wall condition is considered. Because of the high radar prototype cost, two Golay complementary coded UWB life- detection radars were used in three experiments. For simplicity, the two radars are labeled as radars No.1 and No.2, respectively. The tested human subject is indicated by the dotted frame in the figure. As shown in Figure 6a, the type-I experiment was carried out in an apartment unit with various sundry items such as a refrigerator, TV and desktop computer, and the two Golay complementary coded UWB life-detection radars are placed on the same side of the 27 cm brick wall 1.5 m apart. The tested human subject with weak respiration stood at a distance of 4.7 m from radar No.1 and 4.8 m from radar No.2. The type-II experiment was carried out in a building under construction, as shown in Figure 6b. There was electromagnetic interference caused by signal transmission lines and cars. Two radars were placed on the fourth floor 1 m apart. The width of the reinforced concrete floor was 12 cm. The tested human subject lay on the second floor. The distances between him and radars No.1 and No.2 were 6.7 m and 6.8 m, respectively. The type-III experiment was carried out in a stadium, as shown in Figure 6c. An air conditioner and high-power lighting facilities were the main sources of electromagnetic interference. The two radars were placed on the same side of a 37 cm brick wall 2.2 m apart. The tested human subject stood behind the wall, at a distance of 21.6 m from radar No.1 and 21.3 m from radar No.2. Appl. Sci. 2021, 11, 424 9 of 14 Appl. Sci. 2021, 11, x FOR PEER REVIEW 9 of 14 Figure 6. Experimental scenes: (a) type-I scene, (b) type-II scene, (c) type-III scene. Figure 6. Experimental scenes: (a) type-I scene, (b) type-II scene, (c) type-III scene. Normally, the echoes from a single radar can be processed by any fast Fourier trans- Normally, the echoes from a single radar can be processed by any fast Fourier trans- form (FFT)-based respiration detection method—for example, the one in [33]—to extract form (FFT)-based respiration detection method—for example, the one in [33]—to extract the the human breathing frequency. However, due to the low signal to noise ratio (SNR) in human breathing frequency. However, due to the low signal to noise ratio (SNR) in these these three types of experimental scenes, as shown in Figure 7, all the output range–fre- three types of experimental scenes, as shown in Figure 7, all the output range–frequency quency images are too noisy to distinguish the vital sign features (VSFs). Under the archi- images are too noisy to distinguish the vital sign features (VSFs). Under the architecture of tecture of the proposed multiple observation point detection system, the echoes from the the proposed multiple observation point detection system, the echoes from the two Golay two Golay complementary coded UWB life-detection radars were suitable for association complementary coded UWB life-detection radars were suitable for association processing processing to generate the new form of the VSF. to generate the new form of the VSF. Appl. Sci. 2021, 11, x FOR PEER REVIEW 10 of 14 Appl. Sci. 2021, 11, 424 10 of 14 Figure 7. The output range-frequency images using the normal fast Fourier transform (FFT)-based respiration detection Figure 7. The output range-frequency images using the normal fast Fourier transform (FFT)-based respiration detection methods using the echoes from a single radar. (a,b) Type-I experimental results. (c,d) Type-II experimental results. (e,f) methods using the echoes from a single radar. (a,b) Type-I experimental results. (c,d) Type-II experimental results. (e,f) Type-III experimental results. Type-III experimental results. According to our proposed multi-observation point detection method, when weak According to our proposed multi-observation point detection method, when weak respiration movement is present, a new type of VSFs of the tested human subject may respiration movement is present, a new type of VSFs of the tested human subject may appear in the output cross-correlated range–frequency 3D image. The three dimensions appear in the output cross-correlated range–frequency 3D image. The three dimensions of the output cross-correlated range–frequency 3D image represent the range from radar of the output cross-correlated range–frequency 3D image represent the range from radar No.1, the range from radar No.2 and the respiration frequency, respectively. As shown in No.1, the range from radar No.2 and the respiration frequency, respectively. As shown in Figure 8a–c, by finding a suitable threshold, the VSFs for the three types of experiments Figure 8a–c, by finding a suitable threshold, the VSFs for the three types of experiments are remarkable. The respiration frequencies indicated by the VSFs are 0.32 Hz, 0.3 Hz are remarkable. The respiration frequencies indicated by the VSFs are 0.32 Hz, 0.3 Hz and and 0.28 Hz, respectively, which are consistent with the actual situation. To further un- 0.28 Hz, respectively, which are consistent with the actual situation. To further under- derstand the VSFs in the output cross-correlated range–frequency 3D images, 2D slice stand the VSFs in the output cross-correlated range–frequency 3D images, 2D slice (range (range  range) images corresponding to the specific respiration frequencies of 0.32 Hz, × range) images corresponding to the specific respiration frequencies of 0.32 Hz, 0.3 Hz 0.3 Hz and 0.28 Hz are presented in Figure 9a–c, respectively. and 0.28 Hz are presented in Figure 9a–c, respectively. Appl. Sci. 2021, 11, 424 11 of 14 Appl. Sci. 2021, 11, x FOR PEER REVIEW 11 of 14 Figure 8. The output cross-correlated range-frequency 3D images. The vital sign features (VSFs) Figure 8. The output cross-correlated range-frequency 3D images. The vital sign features (VSFs) are are marked in green for (a) the type-I, (b) the type-II and (c) the type-III experiments, respectively. marked in green for (a) the type-I, (b) the type-II and (c) the type-III experiments, respectively. Depending on some threshold detectors, the maximum projection for each range di- Depending on some threshold detectors, the maximum projection for each range mension can be used to extract the values of distances from radars No.1 and No.2 based dimension can be used to extract the values of distances from radars No.1 and No.2 based on the VSFs. on the VSFs. For the type-I experiment, the distances from radars No.1 and No.2 are 4.72 m and For the type-I experiment, the distances from radars No.1 and No.2 are 4.72 m and 4.82 4.82 m, m, respectively respectively . For . Fothe r thtype-II e type-II experiment, experiment the , thdistances e distances fr om from radars radarNo.1 s No.1 an and d No.2 No.2 arar e 6.75 e 6.75 mm and and 6.82 6.82 m, m, respectively respectively . For . For the thtype-III e type-IIexperiment, I experiment, the thdistances e distances fr om from radars radars No.1 and No.2 are 21.61 m and 21.28 m, respectively. Thus, the locations of the tested No.1 and No.2 are 21.61 m and 21.28 m, respectively. Thus, the locations of the tested human human can can be be determined determined by by substituting substituting these these distances distances into into a a “triangulation “triangulation method”. method”. Note that the locations of the tested human subject in the three types of experiments would Note that the locations of the tested human subject in the three types of experiments be uncertain when using only the echoes from a single radar. would be uncertain when using only the echoes from a single radar. Appl. Sci. 2021, 11, 424 12 of 14 Appl. Sci. 2021, 11, x FOR PEER REVIEW 12 of 14 Figure 9. The output 2D slice (range  range) images for the specific respiration frequencies in (a) Figure 9. The output 2D slice (range × range) images for the specific respiration frequencies in (a) the type-I, (b) the type-II and (c) the type-III experiments, respectively. the type-I, (b) the type-II and (c) the type-III experiments, respectively. For the type-I experiment, the distances from radars No.1 and No.2 are 4.72 m and For the type-I experiment, the distances from radars No.1 and No.2 are 4.72 m and 4.82 m, respectively. For the type-II experiment, the distances from radars No.1 and No.2 4.82 m, respectively. For the type-II experiment, the distances from radars No.1 and No.2 are 6.75 m and 6.82 m, respectively. For the type-III experiment, the distances from radars are 6.75 m and 6.82 m, respectively. For the type-III experiment, the distances from radars No.1 and No.2 are 21.61 m and 21.28 m, respectively. Thus, the locations of the tested No.1 and No.2 are 21.61 m and 21.28 m, respectively. Thus, the locations of the tested human can be determined by substituting these distances into a “triangulation method”. human can be determined by substituting these distances into a “triangulation method”. Note that the locations of the tested human subject in the three types of experiments would Note that the locations of the tested human subject in the three types of experiments be uncertain using only the echoes from a single radar. would be uncertain using only the echoes from a single radar. 7. Conclusions 7. Conclusions In this paper, a novel multi-observation point detection system composed of multiple In this paper, a novel multi-observation point detection system composed of multiple Golay complementary coded UWB life-detection radars is proposed, and its performance Golay complementary coded UWB life-detection radars is proposed, and its performance in the context of weak human respiration detection is evaluated in complex through-wall in the context of weak human respiration detection is evaluated in complex through-wall conditions The experiments show that the radar system has excellent detection performance: conditions The experiments show that the radar system has excellent detection perfor- in the through-the-wall scenario, the radar system could detect a respiring target over mance: in the through-the-wall scenario, the radar system could detect a respiring target 21 m distant, while in the through-the-floor setting, it could detect weak breathing target over 21 m distant, while in the through-the-floor setting, it could detect weak breathing behind two levels of reinforced concrete floors. Both the design and the coordination of the Appl. Sci. 2021, 11, 424 13 of 14 Golay complementary coded UWB life-detection radar are illustrated in detail. Remarkably, in the complex through-wall conditions, the weak respiration movement of the tested human subject could be distinguished by the new VSFs that appeared in the output range– frequency 3D image. Study of upgrades to the system, the topology of multiple observation points and the optimized detection algorithm will be future work in this area. Author Contributions: The research was performed by the authors as follows: Conceptualization, K.Y.; methodology, K.Y. and S.W.; software, K.Y. and S.W.; validation, K.Y. and S.Y.; formal analysis, S.W.; investigation, K.Y. and G.F.; resources, G.F.; data curation, K.Y. and S.Y.; writing—original draft preparation, K.Y.; writing—review and editing, S.W.; visualization, K.Y.; supervision, G.F.; project administration, S.W.; funding acquisition, G.F. All authors have read and agreed to the published version of the manuscript. Funding: This research work was supported by the National Science Foundation of China under Grant 61971397 and 61501424, the National Key R&D Program of China under Grant 2018YFC0810202. The authors wish to express their gratitude to the editor and the anonymous reviewers. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable Conflicts of Interest: The authors declare no conflict of interest. References 1. Yilmaz, B.; Ozdemir, C. A radar sensor design and prototype for through-the-wall imaging radar applications. In Proceedings of the Radar Methods & Systems Workshop (IEEE), Kiev, Ukraine, 27–28 September 2016; pp. 60–63. [CrossRef] 2. Wang, X.; Li, G.; Liu, Y.; Amin, M.G. Two-level block matching pursuit for polarimetric through-wall radar imaging. IEEE Trans. Geosci. Remote Sens. 2018, 56, 1533–1545. [CrossRef] 3. Yang, S.; Qin, H.; Liang, X.; Gulliver, T.A. Clutter Elimination and Harmonic Suppression of Non-Stationary Life Signs for Long-Range and Through-Wall Human Subject Detection Using Spectral Kurtosis Analysis (SKA)-Based Windowed Fourier Transform (WFT) Method. Appl. Sci. 2019, 9, 355. [CrossRef] 4. Costanzo, S. Software-Defined Doppler Radar Sensor for Human Breathing Detection. Sensors 2019, 19, 3085. [CrossRef] [PubMed] 5. Sakamoto, T.; Imasaka, R.; Taki, H.; Sato, T.; Yoshioka, M.; Inoue, K.; Fukuda, T.; Sakai, H. Accurate heartbeat monitoring using ultra-wideband radar. IEICE Electron. Expr. 2015, 12, 20141197. [CrossRef] 6. Liu, X.; Zhang, J.; Guo, R.; Yang, G.; Ye, S.; Wu, S.; Fang, G. A novel interferometry positioning and tracking method with short baseline for UWB through-the-wall radar system. In Proceedings of the 17th International Conference on Ground Penetrating Radar (GPR), Rapperswil, Switzerland, 18–21 June 2018; pp. 1–4. [CrossRef] 7. Shikhsarmast, F.M.; Lyu, T.; Liang, X.; Zhang, H.; Gulliver, T.A. Random-Noise Denoising and Clutter Elimination of Human Respiration Movements Based on an Improved Time Window Selection Algorithm Using Wavelet Transform. Sensors 2019, 19, 95. [CrossRef] 8. Yan, J.; Hong, H.; Zhao, H.; Li, Y.; Gu, C.; Zhu, X. Through-Wall Multiple Targets Vital Signs Tracking Based on VMD Algorithm. Sensors 2016, 16, 1293. [CrossRef] 9. Ding, Y.; Sun, Y.; Zhang, J.; Wang, L. Multiperspective target tracking approach for doppler through-wall radar. IEEE Geosci. Remote. Sens. Lett. 2018, 15, 1020–1024. [CrossRef] 10. Xia, Z.; Fang, G.; Ye, S.; Zhang, Q.; Chen, C.; Yin, H. A novel handheld pseudo random coded UWB radar for human sensing applications. IEICE Electron. Expr. 2014, 11, 20140981. [CrossRef] 11. Ash, M.; Ritchie, M.; Chetty, K. On the application of digital moving target indication techniques to short-range FMCW radar data. IEEE Sens. J. 2018, 18, 4167–4175. [CrossRef] 12. Tantiparimongkol, L.; Phasukkit, P. IR-UWB Pulse Generation Using FPGA Scheme for through Obstacle Human Detection. Sensors 2020, 20, 3750. [CrossRef] 13. Rittiplang, A.; Phasukkit, P.; Orankitanun, T. Optimal Central Frequency for Non-Contact Vital Sign Detection Using Monocycle UWB Radar. Sensors 2020, 20, 2916. [CrossRef] 14. Ahmad, F.; Zhang, Y.; Amin, M.G. Three-dimensional wideband beamforming for imaging through a single wall. IEEE Geosci. Remote. Sens. Lett. 2008, 5, 176–179. [CrossRef] 15. Xu, Y.; Wu, S.; Shao, J.; Chen, J.; Fang, G. Life detection and location by MIMO Ultra wideband radar. In Proceedings of the 14th International Conference on Ground Penetrating Radar (GPR), Shanghai, China, 4–8 June 2012; pp. 80–84. [CrossRef] 16. Liu, J.; Kong, L.; Yang, X.; Liu, Q.H. First-order multipath ghosts’ characteristics and suppression in MIMO through-wall imaging. IEEE Geosci. Remote. Sens. Lett. 2017, 13, 1315–1319. [CrossRef] 17. Rittiplang, A.; Phasukkit, P. 1-Tx/5-Rx Through-Wall UWB Switched-Antenna-Array Radar for Detecting Stationary Humans. Sensors 2020, 20, 6828. [CrossRef] [PubMed] Appl. Sci. 2021, 11, 424 14 of 14 18. Liang, F.; Qi, F.; An, Q.; Lv, H.; Chen, F.; Li, Z.; Wang, J. Detection of Multiple Stationary Humans Using UWB MIMO Radar. Sensors 2016, 16, 1922. [CrossRef] 19. Geng, Z. Evolution of netted radar systems. IEEE Access 2020, 8, 124961–124977. [CrossRef] 20. Jia, Y.; Guo, Y.; Yan, C.; Sheng, H.; Zhong, X. Detection and localization for multiple stationary human targets based on cross-correlation of dual-station SFCW radars. Remote Sens. 2019, 11, 1428. [CrossRef] 21. Hunt, A.R. Image formation through walls using a distributed radar sensor array. In Proceedings of the 32nd Applied Imagery Pattern Recognition Workshop, Washington, DC, USA, 15–17 October 2003; pp. 232–237. [CrossRef] 22. Debes, C.; Amin, M.G.; Zoubir, A.M. Target detection in single- and multiple-view through-the-wall radar imaging. IEEE Trans. Geosci. Remote Sens. 2009, 47, 1349–1361. [CrossRef] 23. Sachs, J.; Ley, S.; Just, T.; Chamaani, S.; Helbig, M. Differential Ultra-Wideband Microwave Imaging: Principle Application Challenges. Sensors 2018, 18, 2136. [CrossRef] 24. Galajda, P.; Pecovsky, M.; Sokol, M.; Kmec, M.; Kocur, D. Recent Advances in ASIC Development for Enhanced Performance M-Sequence UWB Systems. Sensors 2020, 20, 4812. [CrossRef] 25. Xia, Z.; Zhang, Q.; Ye, S.; Wang, Y.; Chen, C.; Yin, H.; Fang, G. A novel low-frequency coded ground penetrating radar for deep detection. IEICE Electron. Expr. 2015, 12, 20150200. [CrossRef] 26. Golay, M.J.E. Complementary series. IEEE Trans. Inf. Theory 1961, 7, 82–87. [CrossRef] 27. Liu, X.; Yan, K.; Chen, Z.; Li, C.; Zhang, J.; Ye, S.; Fang, G. A m-sequence UWB radar system design and contrast test with an impulse radar. In Proceedings of the 17th International Conference on Ground Penetrating Radar (GPR), Rapperswil, Switzerland, 18–21 June 2018; pp. 1–4. [CrossRef] 28. Texas Instruments. Time Synchronization over the Air. Available online: http://software-dl.ti.com/simplelink/esd/simplelink_ cc13x0_sdk/3.20.00.23/exports/docs/proprietary-rf/proprietary-rf-users-guide/proprietary-rf/time-synchronization.html (accessed on 6 June 2020). 29. Novoselov, S.; Donskov, O. Distributed local positioning system using DWM1000 location chip. In Proceedings of the 4th International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T), Kharkov, Ukraine, 10–13 October 2017; pp. 489–492. [CrossRef] 30. Gu, Y.; Yang, B. Clock compensation two-way ranging (CC-TWR) based on ultra-wideband communication. In Proceedings of the 8th International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC), Harbin, China, 19–21 July 2018; pp. 1145–1150. [CrossRef] 31. Zhang, H.; Zhang, Z.; Gao, N.; Xiao, Y.; Meng, Z.; Li, Z. Cost-Effective Wearable Indoor Localization and Motion Analysis via the Integration of UWB and IMU. Sensors 2020, 20, 344. [CrossRef] [PubMed] 32. Sang, C.L.; Steinhagen, B.; Homburg, J.D.; Adams, M.; Hesse, M.; Rückert, U. Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods. Appl. Sci. 2020, 10, 3980. [CrossRef] 33. Liu, X.; Yan, K.; Yang, G.; Ye, S.; Fang, G. Improved vital signal extraction algorithm for through the wall radar. Electron. Lett. 2019, 56, 89–91. [CrossRef] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Sciences Multidisciplinary Digital Publishing Institute

A Novel Wireless-Netted UWB Life-Detection Radar System for Quasi-Static Person Sensing

Yan, Kun; Wu, Shiyou; Ye, Shengbo; Fang, Guangyou
Applied Sciences , Volume 11 (1) – Jan 4, 2021
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applied sciences Article A Novel Wireless-Netted UWB Life-Detection Radar System for Quasi-Static Person Sensing 1 , 2 , 3 1 , 2 , 1 , 2 1 , 2 Kun Yan , Shiyou Wu *, Shengbo Ye and Guangyou Fang Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China; yorkstudio@foxmail.com (K.Y.); shengboye@163.com (S.Y.); gyfang@mail.ie.ac.cn (G.F.) The Key Laboratory of Electromagnetic Radiation and Sensing Technology, Chinese Academy of Science, Haidian District North 4th Ring West Road 19th, Beijing 100190, China School of Electronic, Electrical and Communication Engineering, University of the Chinese Academy of Sciences, Beijing 100039, China * Correspondence: wusy@aircas.ac.cn Abstract: In actual life-detection radar applications, a quasi-static person with weak respiration is difficult to find when relying on the echoes from a single fixed observation point. To effectively sense the weak respiration of a quasi-static person in complex through-wall and through-floor conditions, this paper proposes a novel multi-observation point detection system composed of multiple Golay complementary coded radars in which communication and synchronization are carried out wirelessly. The collaboration structure and Golay complementary coded transmitter improve the signal to noise ratio (SNR). Proof-of-principle experiments are carried out with our designed radar prototype and prove that the radar system can detect a respiring target 21 m behind a brick wall or a respiring target behind two levels of reinforced concrete floors, validating the effectiveness of a multi-observation point working mode for the efficient detection of weak human respiration. Keywords: life detection radar; multi-observation point system; through-the-wall radar; Golay complementary code Citation: Yan, K.; Wu, S.; Ye, S.; Fang, G. A Novel Wireless-Netted UWB Life-Detection Radar System for Quasi-Static Person Sensing. Appl. Sci. 1. Introduction 2021, 11, 424. https://doi.org/ In rescue applications, there is increasing demand for mechanisms to improve the 10.3390/app11010424 capability to sense trapped persons in complex scenes. Ultra-wideband (UWB) radar tech- nology provides strong anti-interference ability, high-range resolution and penetrability, Received: 9 December 2020 which plays an important role in detecting trapped persons [1–4]. It can detect the vital Accepted: 30 December 2020 signs of trapped quasi-static persons with weak micro-movements [5–8]. However, the de- Published: 4 January 2021 tection capability of single fixed observation point systems is limited in complex conditions. In order to increase the detection capabilities and detection rate of weak respiration targets Publisher’s Note: MDPI stays neu- in complex environments, novel transmit signal designs are proposed for higher signal tral with regard to jurisdictional clai- to noise ratios (SNRs) in [9–13]. The multiple input–multiple output (MIMO) technology, ms in published maps and institutio- which uses a real aperture with multiple transceiver combinations, presents an instant nal affiliations. imaging resolution and high clutter suppression capability, and it has been widely used in [14–18]. Further, the multi-view and netted radar systems were found to improve the SNR and increase rescue efficiency in [19–22]. The development of UWB radar vital sign Copyright: © 2021 by the authors. Li- detection techniques that allow multi-point observation and data association processing is censee MDPI, Basel, Switzerland. becoming a priority, motivating the design of multi-observation point detection systems This article is an open access article that increase rescue efficiency. distributed under the terms and con- In this paper, we propose a novel multi-observation point detection system composed ditions of the Creative Commons At- of multiple Golay complementary coded UWB life-detection radars. The communication tribution (CC BY) license (https:// and synchronization between radars are carried out wirelessly. For the echoes received creativecommons.org/licenses/by/ at different observation points, to improve the SNR of vital signs, the cross-correlation 4.0/). Appl. Sci. 2021, 11, 424. https://doi.org/10.3390/app11010424 https://www.mdpi.com/journal/applsci Appl. Sci. 2021, 11, x FOR PEER REVIEW 2 of 14 Appl. Sci. 2021, 11, 424 2 of 14 received at different observation points, to improve the SNR of vital signs, the cross-cor- relation operation is performed in the slow-time dimension. Meanwhile, the use of a low operation is performed in the slow-time dimension. Meanwhile, the use of a low sidelobe sidelobe sinusoidal modulation Golay complementary coded signal with a center fre- sinusoidal modulation Golay complementary coded signal with a center frequency of about quency of about 1 GHz as the transmit signal can further improve the SNR. In addition, 1 GHz as the transmit signal can further improve the SNR. In addition, the self-positioning the self-positioning technology is deployed in each UWB life-detection radar to make their technology is deployed in each UWB life-detection radar to make their positions known. positions known. Finally, three experiments are carried out involving penetrating walls Finally, three experiments are carried out involving penetrating walls and reinforced and reinforced concrete floors, verifying the detection performance of the proposed multi- concrete floors, verifying the detection performance of the proposed multi-observation observation point detection system. point detection system. 2. Multi-Observation Point Detection System 2. Multi-Observation Point Detection System As shown in Figure 1, the architecture of the proposed wireless-netted multi-obser- As shown in Figure 1, the architecture of the proposed wireless-netted multi-observation vation point detection system includes several independent single-channel Golay comple- point detection system includes several independent single-channel Golay complementary mentary coded UWB life-detection radars, which are taken as the network nodes con- coded UWB life-detection radars, which are taken as the network nodes controlled by a co tro ntll ro ed l h by ost a . T ch ont e rrol ada host rs a.r e Tr he esp rad onar sib s lare e forespon r the ge si nb ele ra f tor ionth , te ra gener nsmis at si io on n, atr nansmi d reces psi tion on and of th recept e Gola ion y co of m tp he lem Go en lay ta rcom y coplemen ded sign tary al. T coded he det sa ig iln ed al. dTh esie gn deta of t il h ee d sdesign ystem io s f dth isc e u sys ssetem d in is Se dis ctic ouss n 3e . d Th in e Section control h 3. oTh st ie s con resp tro onls host ible fio s rrtespon he hus m ibl an e –fo mr ath che in hum e intan erf– am ce achi , col n la e b io nt ra erfa tive ce, ac coll quiabora sitiontive andacquisition data proce an ssid ng d.ata Bypro usice ng ssi tim ng. e d By iv u issi io n n g m tim ule ti p dl ivi exsi in o g n (mu TDM ltiplex ) and intg he (TDM) star topology, the control host coordinates these network nodes during the multiple observation and the star topology, the control host coordinates these network nodes during the mul- point detection process to prevent mutual interference. tiple observation point detection process to prevent mutual interference. Figure 1. The wireless-netted multi-observation point detection architecture. Figure 1. The wireless-netted multi-observation point detection architecture. 3. Design of Golay Complementary Coded Radar 3. Design of Golay Complementary Coded Radar The block diagram of the Golay complementary coded radar for a multi-observation The block diagram of the Golay complementary coded radar for a multi-observation point detection system is shown in Figure 2. There are six key components: a digital point detection system is shown in Figure 2. There are six key components: a digital trans- transmitter, a dual-channel receiver, a clock manager, a network clock, a self-positioning mitter, a dual-channel receiver, a clock manager, a network clock, a self-positioning mod- module and a wireless communication module. A Xilinx Artix-7 Field Programmable Gate ule and a wireless communication module. A Xilinx Artix-7 Field Programmable Gate Array (FPGA) is used as the main controller unit to manage the peripherals of the radar. To Array (FPGA) is used as the main controller unit to manage the peripherals of the radar. reduce the system cost and improve the spurious-free dynamic range, the equivalent-time To reduce the system cost and improve the spurious-free dynamic range, the equivalent- sampling technique is adopted. A 16-bit analog to digital converter (ADC) with a maximal time sampling technique is adopted. A 16-bit analog to digital converter (ADC) with a sampling rate of 160 Mbps and a full power bandwidth of 1.4 GHz is used as the receiver. maximal sampling rate of 160 Mbps and a full power bandwidth of 1.4 GHz is used as the A pair of bow-tie antennas are used for electromagnetic radiation and reception. A W5300 receiver. A pair of bow-tie antennas are used for electromagnetic radiation and reception. chip is used to exchange the raw radar data and commands with the control host by the A W5300 chip is used to exchange the raw radar data and commands with the control host TCP network protocol, and the wireless bridge realizes the wireless communication. In the by the TCP network protocol, and the wireless bridge realizes the wireless communica- following, the Golay complementary coded signal transmitter (marked A in Figure 2), the tion. In the following, the Golay complementary coded signal transmitter (marked A in network clock module (marked D in Figure 2), the self-positioning module (marked E in Figure 2) and antennas are described in detail. Appl. Sci. 2021, 11, x FOR PEER REVIEW 3 of 14 Appl. Sci. 2021, 11, 424 3 of 14 Figure 2), the network clock module (marked D in Figure 2), the self-positioning module (marked E in Figure 2) and antennas are described in detail. 2GHz Signal 14bit Power LPF LNA TX Generator DAC LPF Splitter Controller PART A. The Transmitter 125MHz SPI Double Equivalent- Programmable Delay Line LPF Channel time Sampler 16bit RX LPF LNA ADC Controller PART B. The Equivalent-Time Sampler 2GHz DAC PLL+VCO Single Board 125MHz FPGA 50MHz Clock Divider(2) Divider(8) Synchronizati 125MHz Delay SPI Divider(2) Divider(8) on Controller Line PART C. The Single Board Synchronous Module CC1310 RF Net Clock SPI 433MHz Two-way Circuitry Controller Antenna Synchronization Algorithm Counter PART D. Net Clock Synchronous Module STM32 MCU DWM1000 Self- SPI Double-sided Two-way positioning SPI UWB Ranging Algorithm RF Circuitry Controller Antenna PART E. Self-positioning Module WIFI Data W5300 Wireless Net RJ45 Transmission 16bit WIFI Bridge TCP Protocol Unit Antenna PART F. Data Transmission Module Figu Figure re 2. 2. The b The block lock d diagram iagram of of the Golay the Golay c complementary omplementary co coded ded radar radar. . UWB: UWB: ultra-wideband. ultra-wideband. 3.1. The Golay Complementary Coded Signal Transmitter 3.1. The Golay Complementary Coded Signal Transmitter The selection of the transmitting signal is a key element of the radar system. The The selection of the transmitting signal is a key element of the radar system. The pseudo-random sequence has been widely applied to modulate the transmitting signal pseudo-random sequence has been widely applied to modulate the transmitting signal of of through-the-wall radar due to its high signal to noise ratio (SNR) and high range through-the-wall radar due to its high signal to noise ratio (SNR) and high range resolu- resolution [23,24]. Of these, the m-sequence, Gold sequence and Golay complementary tion [23,24]. Of these, the m-sequence, Gold sequence and Golay complementary sequence sequence are most commonly used. The m-sequence has long been applied to UWB radars are most commonly used. The m-sequence has long been applied to UWB radars due to due to its good autocorrelation characteristics. However, the number of m-sequences its good autocorrelation characteristics. However, the number of m-sequences is small, so is small, so alternative sequences of a fixed- length are limited. The Gold sequence is alternative sequences of a fixed- length are limited. The Gold sequence is based on the m- based on the m-sequence and has low autocorrelation but better cross-correlation than the sequence and has low autocorrelation but better cross-correlation than the m-sequence. m-sequence. There are a greater number of sequences, which can be used for multiple There are a greater number of sequences, which can be used for multiple input–multiple input–multiple output (MIMO) radars. The Golay complementary sequence contains two output (MIMO) radars. The Golay complementary sequence contains two sub-codes; the sub-codes; the autocorrelation functions of the two sub-codes can be added together to autocorrelation functions of the two sub-codes can be added together to eliminate the eliminate the side-lobes. The peak side-lobe ratio is better than that of the m-sequence, but side-lobes. The peak side-lobe ratio is better than that of the m-sequence, but the Golay the Golay complementary sequence’s time efficiency is low. complementary sequence’s time efficiency is low. Determining trapped human beings from raw radar echoes is extremely difficult Determining trapped human beings from raw radar echoes is extremely difficult due due to micro-motion and the low reflectivity of the human body. The autocorrelation to micro-motion and the low reflectivity of the human body. The autocorrelation charac- characteristic is the essential reference standard for selecting the pseudo random code. In teristic is the essential reference standard for selecting the pseudo random code. In com- combination with our previous research work [25], setting the Golay complementary coded bination with our previous research work [25], setting the Golay complementary coded signal as the transmitting signal can avoid the weak reflection target’s echo signal being signal as the transmitting signal can avoid the weak reflection target’s echo signal being submerged by the side-lobes due to its high peak side-lobe ratio, thus further improving submerged by the side-lobes due to its high peak side-lobe ratio, thus further improving the penetration capability and anti-noise capability of the radar. the penetration capability and anti-noise capability of the radar. Consider a binary sequence a = [a , a , a ,] , where N means the length of 0 1 N1 Consider a binary sequence 𝐚 = [𝑎 , 𝑎 , ⋯ 𝑎 , ] , where N means the length of se- 0 1 𝑁 −1 sequence a; its aperiodic autocorrelation is defined as quence 𝐚 ; its aperiodic autocorrelation is defined as N1k 𝑁 −1−𝑘 C (k) = a(j)a(j + k), 0  k  N 1. (1) a,a å ( ) ( ) 𝐶 𝑘 = ∑ 𝑎 𝑗 𝑎 (𝑗 + 𝑘 ) , 0 ≤ 𝑘 ≤ 𝑁 − 1. (1) 𝑎 ,𝑎 j=0 𝑗 =0 Artix-7 FPGA Appl. Sci. 2021, 11, 424 4 of 14 Appl. Sci. 2021, 11, x FOR PEER REVIEW 4 of 14 Consider two sequences with the length of N: a = [a , a , a ,] and b = Consider two sequences with the length of N: 𝐚 = [𝑎 , 𝑎 , ⋯ 𝑎 , ] and 𝐛 = 0 1 N1 0 1 𝑁 −1 [[b𝑏 , ,b𝑏 , ,⋯ 𝑏b ,,]] ,, 𝑎a ,, 𝑏 b ∈2 {1f , 0 1, }. 0Ifg. 𝐚 If and a and 𝐛 b sati satisfy sfy the the requirement requirement of of (2), (2), the they y can can be 00 11 𝑁N− 11 i𝑖 𝑖 i be called a Golay complementary pair. called a Golay complementary pair. 2𝑁 , 𝑘 = 0 𝐶 (𝑘 ) + 𝐶 (𝑘 ) = { 2N, k = 0 (2) 𝑎 ,𝑎 𝑏 ,𝑏 C (k) + C 0 , 𝑘 (k≠) = 0 (2) a,a b,b 0 , k 6= 0 The Golay complementary pair has good autocorrelation characteristics with no side- The Golay complementary pair has good autocorrelation characteristics with no side- lobes, and the signal peak is doubled. Any sequence included in a Golay complementary lobes, and the signal peak is doubled. Any sequence included in a Golay complementary pair is called a Golay complementary sequence [26]. The signal modulated by sequence 𝐚 pair is called a Golay complementary sequence [26]. The signal modulated by sequence a can be called Golay coded signal A, while the signal modulated by sequence 𝐛 can be can be called Golay coded signal A, while the signal modulated by sequence b can be called called Golay coded signal B. In our proposed system, a single radar only has one trans- Golay coded signal B. In our proposed system, a single radar only has one transceiver ceiver channel for electromagnetic detection; the radar uses Golay coded signal A, and channel for electromagnetic detection; the radar uses Golay coded signal A, and Golay Golay coded signal B executes electromagnetic detection in turn, and their aperiodic au- coded signal B executes electromagnetic detection in turn, and their aperiodic autocor- tocorrelation sum forms the radar A-scan. It should be noted that the high autocorrelation relation sum forms the radar A-scan. It should be noted that the high autocorrelation characteristic of the Golay complementary sequence comes at the expense of time effi- characteristic of the Golay complementary sequence comes at the expense of time efficiency, ciency, which causes a lower scan rate of the radar system than other pseudo random which causes a lower scan rate of the radar system than other pseudo random sequences. sequences. Due to the division of time slices in the TDM netted radar system, a single Due to the division of time slices in the TDM netted radar system, a single radar ’s scanning radar’s scanning rate is further reduced, which causes limited detectability for high-fre- rate is further reduced, which causes limited detectability for high-frequency movements. quency movements. The balance between the number of observation points and radar The balance between the number of observation points and radar scan rate should be scan rate should be considered during system design. In practical terms, the netted radar considered during system design. In practical terms, the netted radar system scan rate system scan rate proposed in the article is about 32 fps (see Section 4), which can detect proposed in the article is about 32 fps (see Section 4), which can detect human breathing human breathing effectively. effectively. As shown in Figure 2a, the Golay complementary coded signal transmitter consists of As shown in Figure 2a, the Golay complementary coded signal transmitter consists a controller in FPGA, a 14-bit digital to analog converter (DAC) at a 2.5 GSPS update rate, of a controller in FPGA, a 14-bit digital to analog converter (DAC) at a 2.5 GSPS update two low-pass filters, a power divider and a low-noise amplifier with 21 dB gain. In order to rate, two low-pass filters, a power divider and a low-noise amplifier with 21 dB gain. In adequately utilize the bandwidth of the antenna and emit more energy, the Golay comple- order to adequately utilize the bandwidth of the antenna and emit more energy, the Golay mentary sequence is modulated by a sinusoidal signal. Meanwhile, to balance the penetra- complementary sequence is modulated by a sinusoidal signal. Meanwhile, to balance the tion ability and the miniaturization of the antenna [27], the Golay complementary sequence penetration ability and the miniaturization of the antenna [27], the Golay complementary with ~1 ns impulse width and a center frequency of ~1 GHz is adopted here. sequence with ~1 ns impulse width and a center frequency of ~1 GHz is adopted here. For the modulation of the Golay complementary coded signal, the value “1” in the se- For the modulation of the Golay complementary coded signal, the value “1” in the quence represents a single positive impulse, while the value “0” represents a single negative sequence represents a single positive impulse, while the value “0” represents a single impulse. Instead of storing the transmitted waveform directly, both the single positive im- negative impulse. Instead of storing the transmitted waveform directly, both the single pulse and the single negative impulse are stored by bit. In this way, fewer ROMs of FPGA positive impulse and the single negative impulse are stored by bit. In this way, fewer are required. A power divider divides the filtered Golay complementary coded signal into ROMs of FPGA are required. A power divider divides the filtered Golay complementary two identical signals: one is fed to the transmitting antenna for radiation, while the other is coded signal into two identical signals: one is fed to the transmitting antenna for radiation, fed to the first channel of the receiver to obtain the reference signal for the pulse compres- while the other is fed to the first channel of the receiver to obtain the reference signal sion. The transmitted signal is shown in Figure 3a. In Figure 3b, the autocorrelation results for the pulse compression. The transmitted signal is shown in Figure 3a. In Figure 3b, of the Golay complementary coded signal A and signal B are presented, and the sidelobe of the autocorrelation results of the Golay complementary coded signal A and signal B are the sum of both is under -40 dB. presented, and the sidelobe of the sum of both is under 40 dB. Figure 3. (a) The Golay complementary coded signal sampled by the receiver. (b) The autocorrelation result of Golay code Figure 3. (a) The Golay complementary coded signal sampled by the receiver. (b) The autocorrelation result of Golay code A, Golay code B and the sum of both. A, Golay code B and the sum of both. Appl. Sci. 2021, 11, 424 5 of 14 3.2. The Network Clock Module During the multiple observation point detection process, the accurate network clock module is required to avoid mutual interference between radars which need to be activated in their own time-slices; that is, by setting a time margin and executing wireless clock synchronization, the mutual interference between radars can be prevented effectively. Here, as shown in Figure 2d, the network clock module is designed to include a SimpleLink Sub-1 GHz module based on Texas Instruments’ CC1310 chip, a counter and a network clock controller in FPGA. A counter in FPGA begins to count at an interval of 1 microsecond after power-on and plays the role of the local network clock for each radar. For different radars, the corresponding counter (called the network clock) has a different initial offset and clock drift. Assuming that the counter ’s value of the first radar joining the network is m , the counters’ values of the remaining radars can be expressed as m = m +S +T , where 1 n 1 n n n means the radar IDs, S represents the clock drift and T represents the initial offset of n n non-simultaneous start-up. m is set as the time base for all radars to realize the TDM. S can be expressed as S = p*m , where p represents the frequency tolerance of the n n radar ’s main oscillator. For the worst case, a crystal oscillator with 10 ppm frequency tolerance might introduce a 20 microsecond error within 1 s. As the human respiration detection algorithm requires 16 s of sampling echoes for one detection, the minimum time margin for S is 320 us. The measurement process of T can be regarded as wireless clock n n synchronization between radars, and all radars’ local network clocks need to be set to m after the synchronization. Here, the CC1310 SimpleLink Sub-1 Ghz module deployed on each radar is used for wireless clock synchronization. The chip provides a set of timers and taggers for radio operation. By sending and receiving the synchronization request twice, the modules can measure the differences of the local network clocks between two radars via a two-way synchronization algorithm [28], where the algorithm is similar to the Network Time Protocol. In order to avoid mutual interference between radars, the CC1310 synchronization module is activated only once for each detection. Therefore, T can be measured between the first radar (its local network clock is m ) and other radars in turn. In addition, the measurement of the CC1310 has an error of 10 us, which should be added into the time margin. In practical terms, the time margin of the network clock module is set as ~15 ms, which is longer than the above requirements (see Section 4). Thus, the clock drift and measurement error would not make the radar network clock invalid. 3.3. The Self-Positioning Module As the relative positions between radars are needed for human respiration detec- tion, the acquisition of all radars’ positions in the architecture of the proposed multiple observation point detection system cannot be neglected. Normally, the relative positions between radars are measured manually in an actual environment, which is not suitable for field applications because of manual measurement errors and the time required. To overcome this problem, a self-positioning module is deployed in each radar to sense its relative position and upload it to the control host automatically. As shown in Figure 2e, the self-positioning module is composed of a DWM1000 ultra-wideband module, an STM32 Microcontroller Unit (MCU) and a self-positioning controller in FPGA. The DWM1000, as the key component of the self-positioning module, is designed based on Decawave’s DW1000 chip. It integrates antenna, all RF circuitry, power management and clock circuitry into one module, supports four radio frequency bands from 3.5 GHz to 6.5 GHz, provides the function of timestamping and precise control of transmission times and can be used in the two-way ranging or Time Difference of Arrival (TDOA) positioning with an error within 10 cm [29–32]. Here, the two-way ranging is realized by the double-sided two-way ranging algorithm executed in the STM32 MCU to achieve the self-positioning of the radars. Although the workflows of the network clock synchronization module and self- positioning module are similar, it should be noted that the CC1310 and the DWM1000 are not interchangeable; the clock frequency of the CC1310, which serves the timestamp register, Appl. Sci. 2021, 11, x FOR PEER REVIEW 6 of 14 Appl. Sci. 2021, 11, 424 6 of 14 Although the workflows of the network clock synchronization module and self-po- sitioning module are similar, it should be noted that the CC1310 and the DWM1000 are not interchangeable; the clock frequency of the CC1310, which serves the timestamp reg- ister, is too low to provide accurate self-positioning, while the configuration and algo- is too low to provide accurate self-positioning, while the configuration and algorithm of the rithm of the DWM1000 are cumbersome and need to be controlled by an MCU, the ran- DWM1000 are cumbersome and need to be controlled by an MCU, the random response dom response time of which might cause a network clock synchronization error. time of which might cause a network clock synchronization error. 3.4. The Antennas 3.4. The Antennas The size of antennas is essential in radar design. A miniaturized radar is more The size of antennas is essential in radar design. A miniaturized radar is more adapt- adaptable to the environment. A certain compromise was made in this work between able to the environment. A certain compromise was made in this work between band- bandwidth and efficiency when designing the antenna to meet the requirements of using width and efficiency when designing the antenna to meet the requirements of using the the ultra-wideband and having a miniaturized size. Large bandwidth and high efficiency ultra-wideband and having a miniaturized size. Large bandwidth and high efficiency can can be obtained in a smaller size by using a bow-tie antenna. The top-layer patch of the be obtained in a smaller size by using a bow-tie antenna. The top-layer patch of the an- antenna has a semi-elliptical shape, meaning that the current flows through a longer path tenna has a semi-elliptical shape, meaning that the current flows through a longer path in in a small size. Simultaneously, an elliptical end is added to the bottom layer, coinciding a small size. Simultaneously, an elliptical end is added to the bottom layer, coinciding with the top layer. By loading resistors on the bottom side, the current is coupled through with the top layer. By loading resistors on the bottom side, the current is coupled through the substrate for further absorption, which improves the radiation efficiency of the antenna. the substrate for further absorption, which improves the radiation efficiency of the an- The use of a metal back cavity can also enhance the antenna’s forward radiation ability tenna. The use of a metal back cavity can also enhance the antenna’s forward radiation while further improving the isolation of the transmitting and receiving antenna and the ability while further improving the isolation of the transmitting and receiving antenna SNR of the radar system. The size of a single antenna is 140 mm  70 mm  35 mm, as and the SNR of the radar system. The size of a single antenna is 140 mm × 70 mm × 35 mm, Figure 4 shows, and the operating band is 0.5–1.5 GHz. as Figure 4 shows, and the operating band is 0.5–1.5 GHz. Figure 4. The antennas. Figure 4. The antennas. 4. 4.Radar RadarCoordination Coordination The orderly collaboration between radars depends on reasonable time-slice manage- The orderly collaboration between radars depends on reasonable time-slice manage- ment and workflow management; in this work, these were designed in accordance with ment and workflow management; in this work, these were designed in accordance with the radar parameters in Table 1. the radar parameters in Table 1. Table 1. Key parameters of the proposed radar system. ADC: analog to digital converter. Table 1. Key parameters of the proposed radar system. ADC: analog to digital converter. Property Proposed Radar Property Proposed Radar Netted radar system scan rate 32 fps Netted radar system scan rate 32 fps Center frequency of radar system 1 GHz Center frequency of radar system 1 GHz Equivalent sampling frequency (𝐹 ) 16 GSPS Equivalent sampling frequency (F ) 𝑆 16 GSPS Real-time sampling frequency 125 MSPS Real-time sampling frequency 125 MSPS Sampling points (N) 16,384 Sampling points (𝑁 ) 16384 Average times (N ) 32 Average times (𝑁 ) 32 ADC Resolution 16 bits ADC Resolution 16 bits We configured the equivalent-time sampling rate and the real-time sampling rate of the receiver to be 16 GSPS and 125 MSPS, respectively. Accordingly, the receiver was able to Appl. Sci. 2021, 11, x FOR PEER REVIEW 7 of 14 We configured the equivalent-time sampling rate and the real-time sampling rate of the receiver to be 16 GSPS and 125 MSPS, respectively. Accordingly, the receiver was able to obtain 𝑁 = 16384 sampling points for one UWB A-scan in 512 us, equaling 128 pulse repeat periods. To improve the echo’s SNR, 𝑁 = 32 UWB A-scans were required for hu- man respiration detection, and the corresponding time consumption was about 17 ms. In addition, the wireless data transmission for transmitting the radar echoes and the refer- ence signal for pulse compression with a network speed of 100 Mbps required about 6 ms. Thus, the required minimum time slice was about 23 ms. In practical terms, to retain the time margin for wireless transmission and the network clock module, each time slice was set as 32 ms. To understand the orderly collaboration between radars, the workflow timing is pre- sented in Figure 5. (1) Start Up and Netted: As the initialization stage, the host establishes the WIFI network and permits radars access. Radar IDs are distributed by the host for data acquisition, clock synchronization and self-positioning. Appl. Sci. 2021, 11, 424 7 of 14 (2) Idle: All components turn to the standby mode, waiting for the “start command”. (3) Network Clock Synchronization: The network clock synchronization is executed firstly after receiving the “start command”. obtain N = 16,384 sampling points for one UWB A-scan in 512 us, equaling 128 pulse repeat (4) Self-positioning: The relative positions between radars are measured by their self-po- periods. To improve the echo’s SNR, N = 32 UWB A-scans were required for human sitioning modules once. A respiration detection, and the corresponding time consumption was about 17 ms. In (5) Time Slice Allocation: The host verifies the validity of the radars’ topology and allo- addition, the wireless data transmission for transmitting the radar echoes and the reference cates time slices to radars. signal for pulse compression with a network speed of 100 Mbps required about 6 ms. Thus, (6) Electromagnetic Detection: According to the network clock synchronization and the the required minimum time slice was about 23 ms. In practical terms, to retain the time time slice allocation, each radar illuminates the target area and receives the echoes margin for wireless transmission and the network clock module, each time slice was set as within their time slices in turn. 32 ms. (7) Data Transmission: The echoes are uploaded to the host. To understand the orderly collaboration between radars, the workflow timing is (8) Result Output: After 16 s of sampling echoes, the host calculates the target’s position presented in Figure 5. and displays the results on the human–machine interface. Figure 5. The workflow timing. Figure 5. The workflow timing. 5. Algorithm Description for Multi-Observation Point Detection System (1) Start Up and Netted: As the initialization stage, the host establishes the WIFI network and permits radars access. Radar IDs are distributed by the host for data acquisition, Denote S(k, m) as the slow-time range matrix obtained at a single observation point, clock synchronization and self-positioning. where k = 0, 1, …, K−1 is the range cell index, and m = 0, 1, …, M−1 is the slow-time index. (2) Idle: All components turn to the standby mode, waiting for the “start command”. For quasi-static trapped human beings with quasi-periodic but weak respiration, we de- (3) Network Clock Synchronization: The network clock synchronization is executed firstly note Si(k, m) and Sj(k, m) as the output slow-time range matrixes obtained from the ith and after receiving the “start command”. jth (i ≠ j) observation points after eliminating the time-invariant clutter/interference, re- (4) Self-positioning: The relative positions between radars are measured by their self- spectively. Due to the cross-correlation function of the non-periodic noise being prone to positioning modules once. zeroing, the cross-correlation between Si(k, m) and Sj(k, m) is applied to improve the low (5) Time Slice Allocation: The host verifies the validity of the radars’ topology and allocates signal-to-noise ratio (SNR). However, the quasi-periodic component and its harmonic time slices to radars. components contained in the slow-time signal Si(k, m) and Sj(k, m) are still preserved. As- (6) Electromagnetic Detection: According to the network clock synchronization and the sume that the size of Si(k, m) and Sj(k, m) are Ku × Ma and Kv × Mb, respectively. The cross- time slice allocation, each radar illuminates the target area and receives the echoes correlation function Rij(u, v, m) is defined as within their time slices in turn. (7) Data Transmission: The echoes are uploaded to the host. (8) Result Output: After 16 s of sampling echoes, the host calculates the target’s position and displays the results on the human–machine interface. 5. Algorithm Description for Multi-Observation Point Detection System Denote S(k, m) as the slow-time range matrix obtained at a single observation point, where k = 0, 1, . . . , K1 is the range cell index, and m = 0, 1, . . . , M1 is the slow-time index. For quasi-static trapped human beings with quasi-periodic but weak respiration, we denote S (k, m) and S (k, m) as the output slow-time range matrixes obtained from the ith i j and jth (i 6= j) observation points after eliminating the time-invariant clutter/interference, respectively. Due to the cross-correlation function of the non-periodic noise being prone to zeroing, the cross-correlation between S (k, m) and S (k, m) is applied to improve the i j low signal-to-noise ratio (SNR). However, the quasi-periodic component and its harmonic components contained in the slow-time signal S (k, m) and S (k, m) are still preserved. i j Assume that the size of S (k, m) and S (k, m) are K  M and K  M , respectively. The u a v i j b cross-correlation function R (u, v, m) is defined as ij R (u, v, m) = E{S (k , m )S (k , m )} (3) ij i u a j v b Appl. Sci. 2021, 11, 424 8 of 14 AN R (u, v, m) = max {R (u, v, m)}AN {R (u, v, m)} (4) ij m ij m ij where the range cell index k 2 [0, K 1] and k 2 [0, K 1], the slow-time cell index u u v v m 2 [0, M 1], m 2 [0, M 1] and m = m – m . Then, the advance normalization a a b b a b (AN) method is applied to R (u, v, m) so that the weak quasi-periodic component of the ij quasi-static trapped human being can be further enhanced. We denote the output result of AN AN the AN method as R (u, v, m). The Fourier transform of R (u, v, m) is taken in each ij ij slow-time dimension, and the maximum corresponds to the quasi-static trapped human being; i.e., (u , v ) indicates the possible range location of the quasi-static trapped i max j max human being depending on the different ith and jth observation points. AN (u ,v ) = argmax {FFT {R (u, v, m)}}, u 6= v (5) max max uv m i j ij 6. Experiments and Results In this section, three types of experimental scenes are designed to simulate complex through-wall conditions. The corresponding experiments were carried out to verify the performance of the proposed multiple observation point detection system. Two layers of brick walls or reinforced concrete floors were penetrated to detect human respiration in type-I (see Figure 6a) and type-II (see Figure 6b) experimental scenes, respectively. For the type-III scene, the propagation distance effect in the through-wall condition is considered. Because of the high radar prototype cost, two Golay complementary coded UWB life- detection radars were used in three experiments. For simplicity, the two radars are labeled as radars No.1 and No.2, respectively. The tested human subject is indicated by the dotted frame in the figure. As shown in Figure 6a, the type-I experiment was carried out in an apartment unit with various sundry items such as a refrigerator, TV and desktop computer, and the two Golay complementary coded UWB life-detection radars are placed on the same side of the 27 cm brick wall 1.5 m apart. The tested human subject with weak respiration stood at a distance of 4.7 m from radar No.1 and 4.8 m from radar No.2. The type-II experiment was carried out in a building under construction, as shown in Figure 6b. There was electromagnetic interference caused by signal transmission lines and cars. Two radars were placed on the fourth floor 1 m apart. The width of the reinforced concrete floor was 12 cm. The tested human subject lay on the second floor. The distances between him and radars No.1 and No.2 were 6.7 m and 6.8 m, respectively. The type-III experiment was carried out in a stadium, as shown in Figure 6c. An air conditioner and high-power lighting facilities were the main sources of electromagnetic interference. The two radars were placed on the same side of a 37 cm brick wall 2.2 m apart. The tested human subject stood behind the wall, at a distance of 21.6 m from radar No.1 and 21.3 m from radar No.2. Appl. Sci. 2021, 11, 424 9 of 14 Appl. Sci. 2021, 11, x FOR PEER REVIEW 9 of 14 Figure 6. Experimental scenes: (a) type-I scene, (b) type-II scene, (c) type-III scene. Figure 6. Experimental scenes: (a) type-I scene, (b) type-II scene, (c) type-III scene. Normally, the echoes from a single radar can be processed by any fast Fourier trans- Normally, the echoes from a single radar can be processed by any fast Fourier trans- form (FFT)-based respiration detection method—for example, the one in [33]—to extract form (FFT)-based respiration detection method—for example, the one in [33]—to extract the the human breathing frequency. However, due to the low signal to noise ratio (SNR) in human breathing frequency. However, due to the low signal to noise ratio (SNR) in these these three types of experimental scenes, as shown in Figure 7, all the output range–fre- three types of experimental scenes, as shown in Figure 7, all the output range–frequency quency images are too noisy to distinguish the vital sign features (VSFs). Under the archi- images are too noisy to distinguish the vital sign features (VSFs). Under the architecture of tecture of the proposed multiple observation point detection system, the echoes from the the proposed multiple observation point detection system, the echoes from the two Golay two Golay complementary coded UWB life-detection radars were suitable for association complementary coded UWB life-detection radars were suitable for association processing processing to generate the new form of the VSF. to generate the new form of the VSF. Appl. Sci. 2021, 11, x FOR PEER REVIEW 10 of 14 Appl. Sci. 2021, 11, 424 10 of 14 Figure 7. The output range-frequency images using the normal fast Fourier transform (FFT)-based respiration detection Figure 7. The output range-frequency images using the normal fast Fourier transform (FFT)-based respiration detection methods using the echoes from a single radar. (a,b) Type-I experimental results. (c,d) Type-II experimental results. (e,f) methods using the echoes from a single radar. (a,b) Type-I experimental results. (c,d) Type-II experimental results. (e,f) Type-III experimental results. Type-III experimental results. According to our proposed multi-observation point detection method, when weak According to our proposed multi-observation point detection method, when weak respiration movement is present, a new type of VSFs of the tested human subject may respiration movement is present, a new type of VSFs of the tested human subject may appear in the output cross-correlated range–frequency 3D image. The three dimensions appear in the output cross-correlated range–frequency 3D image. The three dimensions of the output cross-correlated range–frequency 3D image represent the range from radar of the output cross-correlated range–frequency 3D image represent the range from radar No.1, the range from radar No.2 and the respiration frequency, respectively. As shown in No.1, the range from radar No.2 and the respiration frequency, respectively. As shown in Figure 8a–c, by finding a suitable threshold, the VSFs for the three types of experiments Figure 8a–c, by finding a suitable threshold, the VSFs for the three types of experiments are remarkable. The respiration frequencies indicated by the VSFs are 0.32 Hz, 0.3 Hz are remarkable. The respiration frequencies indicated by the VSFs are 0.32 Hz, 0.3 Hz and and 0.28 Hz, respectively, which are consistent with the actual situation. To further un- 0.28 Hz, respectively, which are consistent with the actual situation. To further under- derstand the VSFs in the output cross-correlated range–frequency 3D images, 2D slice stand the VSFs in the output cross-correlated range–frequency 3D images, 2D slice (range (range  range) images corresponding to the specific respiration frequencies of 0.32 Hz, × range) images corresponding to the specific respiration frequencies of 0.32 Hz, 0.3 Hz 0.3 Hz and 0.28 Hz are presented in Figure 9a–c, respectively. and 0.28 Hz are presented in Figure 9a–c, respectively. Appl. Sci. 2021, 11, 424 11 of 14 Appl. Sci. 2021, 11, x FOR PEER REVIEW 11 of 14 Figure 8. The output cross-correlated range-frequency 3D images. The vital sign features (VSFs) Figure 8. The output cross-correlated range-frequency 3D images. The vital sign features (VSFs) are are marked in green for (a) the type-I, (b) the type-II and (c) the type-III experiments, respectively. marked in green for (a) the type-I, (b) the type-II and (c) the type-III experiments, respectively. Depending on some threshold detectors, the maximum projection for each range di- Depending on some threshold detectors, the maximum projection for each range mension can be used to extract the values of distances from radars No.1 and No.2 based dimension can be used to extract the values of distances from radars No.1 and No.2 based on the VSFs. on the VSFs. For the type-I experiment, the distances from radars No.1 and No.2 are 4.72 m and For the type-I experiment, the distances from radars No.1 and No.2 are 4.72 m and 4.82 4.82 m, m, respectively respectively . For . Fothe r thtype-II e type-II experiment, experiment the , thdistances e distances fr om from radars radarNo.1 s No.1 an and d No.2 No.2 arar e 6.75 e 6.75 mm and and 6.82 6.82 m, m, respectively respectively . For . For the thtype-III e type-IIexperiment, I experiment, the thdistances e distances fr om from radars radars No.1 and No.2 are 21.61 m and 21.28 m, respectively. Thus, the locations of the tested No.1 and No.2 are 21.61 m and 21.28 m, respectively. Thus, the locations of the tested human human can can be be determined determined by by substituting substituting these these distances distances into into a a “triangulation “triangulation method”. method”. Note that the locations of the tested human subject in the three types of experiments would Note that the locations of the tested human subject in the three types of experiments be uncertain when using only the echoes from a single radar. would be uncertain when using only the echoes from a single radar. Appl. Sci. 2021, 11, 424 12 of 14 Appl. Sci. 2021, 11, x FOR PEER REVIEW 12 of 14 Figure 9. The output 2D slice (range  range) images for the specific respiration frequencies in (a) Figure 9. The output 2D slice (range × range) images for the specific respiration frequencies in (a) the type-I, (b) the type-II and (c) the type-III experiments, respectively. the type-I, (b) the type-II and (c) the type-III experiments, respectively. For the type-I experiment, the distances from radars No.1 and No.2 are 4.72 m and For the type-I experiment, the distances from radars No.1 and No.2 are 4.72 m and 4.82 m, respectively. For the type-II experiment, the distances from radars No.1 and No.2 4.82 m, respectively. For the type-II experiment, the distances from radars No.1 and No.2 are 6.75 m and 6.82 m, respectively. For the type-III experiment, the distances from radars are 6.75 m and 6.82 m, respectively. For the type-III experiment, the distances from radars No.1 and No.2 are 21.61 m and 21.28 m, respectively. Thus, the locations of the tested No.1 and No.2 are 21.61 m and 21.28 m, respectively. Thus, the locations of the tested human can be determined by substituting these distances into a “triangulation method”. human can be determined by substituting these distances into a “triangulation method”. Note that the locations of the tested human subject in the three types of experiments would Note that the locations of the tested human subject in the three types of experiments be uncertain using only the echoes from a single radar. would be uncertain using only the echoes from a single radar. 7. Conclusions 7. Conclusions In this paper, a novel multi-observation point detection system composed of multiple In this paper, a novel multi-observation point detection system composed of multiple Golay complementary coded UWB life-detection radars is proposed, and its performance Golay complementary coded UWB life-detection radars is proposed, and its performance in the context of weak human respiration detection is evaluated in complex through-wall in the context of weak human respiration detection is evaluated in complex through-wall conditions The experiments show that the radar system has excellent detection performance: conditions The experiments show that the radar system has excellent detection perfor- in the through-the-wall scenario, the radar system could detect a respiring target over mance: in the through-the-wall scenario, the radar system could detect a respiring target 21 m distant, while in the through-the-floor setting, it could detect weak breathing target over 21 m distant, while in the through-the-floor setting, it could detect weak breathing behind two levels of reinforced concrete floors. Both the design and the coordination of the Appl. Sci. 2021, 11, 424 13 of 14 Golay complementary coded UWB life-detection radar are illustrated in detail. Remarkably, in the complex through-wall conditions, the weak respiration movement of the tested human subject could be distinguished by the new VSFs that appeared in the output range– frequency 3D image. Study of upgrades to the system, the topology of multiple observation points and the optimized detection algorithm will be future work in this area. Author Contributions: The research was performed by the authors as follows: Conceptualization, K.Y.; methodology, K.Y. and S.W.; software, K.Y. and S.W.; validation, K.Y. and S.Y.; formal analysis, S.W.; investigation, K.Y. and G.F.; resources, G.F.; data curation, K.Y. and S.Y.; writing—original draft preparation, K.Y.; writing—review and editing, S.W.; visualization, K.Y.; supervision, G.F.; project administration, S.W.; funding acquisition, G.F. All authors have read and agreed to the published version of the manuscript. Funding: This research work was supported by the National Science Foundation of China under Grant 61971397 and 61501424, the National Key R&D Program of China under Grant 2018YFC0810202. The authors wish to express their gratitude to the editor and the anonymous reviewers. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable Conflicts of Interest: The authors declare no conflict of interest. References 1. Yilmaz, B.; Ozdemir, C. A radar sensor design and prototype for through-the-wall imaging radar applications. In Proceedings of the Radar Methods & Systems Workshop (IEEE), Kiev, Ukraine, 27–28 September 2016; pp. 60–63. [CrossRef] 2. Wang, X.; Li, G.; Liu, Y.; Amin, M.G. Two-level block matching pursuit for polarimetric through-wall radar imaging. IEEE Trans. Geosci. Remote Sens. 2018, 56, 1533–1545. [CrossRef] 3. Yang, S.; Qin, H.; Liang, X.; Gulliver, T.A. Clutter Elimination and Harmonic Suppression of Non-Stationary Life Signs for Long-Range and Through-Wall Human Subject Detection Using Spectral Kurtosis Analysis (SKA)-Based Windowed Fourier Transform (WFT) Method. Appl. Sci. 2019, 9, 355. [CrossRef] 4. Costanzo, S. Software-Defined Doppler Radar Sensor for Human Breathing Detection. Sensors 2019, 19, 3085. [CrossRef] [PubMed] 5. Sakamoto, T.; Imasaka, R.; Taki, H.; Sato, T.; Yoshioka, M.; Inoue, K.; Fukuda, T.; Sakai, H. Accurate heartbeat monitoring using ultra-wideband radar. IEICE Electron. Expr. 2015, 12, 20141197. [CrossRef] 6. Liu, X.; Zhang, J.; Guo, R.; Yang, G.; Ye, S.; Wu, S.; Fang, G. A novel interferometry positioning and tracking method with short baseline for UWB through-the-wall radar system. In Proceedings of the 17th International Conference on Ground Penetrating Radar (GPR), Rapperswil, Switzerland, 18–21 June 2018; pp. 1–4. [CrossRef] 7. Shikhsarmast, F.M.; Lyu, T.; Liang, X.; Zhang, H.; Gulliver, T.A. Random-Noise Denoising and Clutter Elimination of Human Respiration Movements Based on an Improved Time Window Selection Algorithm Using Wavelet Transform. Sensors 2019, 19, 95. [CrossRef] 8. Yan, J.; Hong, H.; Zhao, H.; Li, Y.; Gu, C.; Zhu, X. Through-Wall Multiple Targets Vital Signs Tracking Based on VMD Algorithm. Sensors 2016, 16, 1293. [CrossRef] 9. Ding, Y.; Sun, Y.; Zhang, J.; Wang, L. Multiperspective target tracking approach for doppler through-wall radar. IEEE Geosci. Remote. Sens. Lett. 2018, 15, 1020–1024. [CrossRef] 10. Xia, Z.; Fang, G.; Ye, S.; Zhang, Q.; Chen, C.; Yin, H. A novel handheld pseudo random coded UWB radar for human sensing applications. IEICE Electron. Expr. 2014, 11, 20140981. [CrossRef] 11. Ash, M.; Ritchie, M.; Chetty, K. On the application of digital moving target indication techniques to short-range FMCW radar data. IEEE Sens. J. 2018, 18, 4167–4175. [CrossRef] 12. Tantiparimongkol, L.; Phasukkit, P. IR-UWB Pulse Generation Using FPGA Scheme for through Obstacle Human Detection. Sensors 2020, 20, 3750. [CrossRef] 13. Rittiplang, A.; Phasukkit, P.; Orankitanun, T. Optimal Central Frequency for Non-Contact Vital Sign Detection Using Monocycle UWB Radar. Sensors 2020, 20, 2916. [CrossRef] 14. Ahmad, F.; Zhang, Y.; Amin, M.G. Three-dimensional wideband beamforming for imaging through a single wall. IEEE Geosci. Remote. Sens. Lett. 2008, 5, 176–179. [CrossRef] 15. Xu, Y.; Wu, S.; Shao, J.; Chen, J.; Fang, G. Life detection and location by MIMO Ultra wideband radar. In Proceedings of the 14th International Conference on Ground Penetrating Radar (GPR), Shanghai, China, 4–8 June 2012; pp. 80–84. [CrossRef] 16. Liu, J.; Kong, L.; Yang, X.; Liu, Q.H. First-order multipath ghosts’ characteristics and suppression in MIMO through-wall imaging. IEEE Geosci. Remote. Sens. Lett. 2017, 13, 1315–1319. [CrossRef] 17. Rittiplang, A.; Phasukkit, P. 1-Tx/5-Rx Through-Wall UWB Switched-Antenna-Array Radar for Detecting Stationary Humans. Sensors 2020, 20, 6828. [CrossRef] [PubMed] Appl. Sci. 2021, 11, 424 14 of 14 18. Liang, F.; Qi, F.; An, Q.; Lv, H.; Chen, F.; Li, Z.; Wang, J. Detection of Multiple Stationary Humans Using UWB MIMO Radar. Sensors 2016, 16, 1922. [CrossRef] 19. Geng, Z. Evolution of netted radar systems. IEEE Access 2020, 8, 124961–124977. [CrossRef] 20. Jia, Y.; Guo, Y.; Yan, C.; Sheng, H.; Zhong, X. Detection and localization for multiple stationary human targets based on cross-correlation of dual-station SFCW radars. Remote Sens. 2019, 11, 1428. [CrossRef] 21. Hunt, A.R. Image formation through walls using a distributed radar sensor array. In Proceedings of the 32nd Applied Imagery Pattern Recognition Workshop, Washington, DC, USA, 15–17 October 2003; pp. 232–237. [CrossRef] 22. Debes, C.; Amin, M.G.; Zoubir, A.M. Target detection in single- and multiple-view through-the-wall radar imaging. IEEE Trans. Geosci. Remote Sens. 2009, 47, 1349–1361. [CrossRef] 23. Sachs, J.; Ley, S.; Just, T.; Chamaani, S.; Helbig, M. Differential Ultra-Wideband Microwave Imaging: Principle Application Challenges. Sensors 2018, 18, 2136. [CrossRef] 24. Galajda, P.; Pecovsky, M.; Sokol, M.; Kmec, M.; Kocur, D. Recent Advances in ASIC Development for Enhanced Performance M-Sequence UWB Systems. Sensors 2020, 20, 4812. [CrossRef] 25. Xia, Z.; Zhang, Q.; Ye, S.; Wang, Y.; Chen, C.; Yin, H.; Fang, G. A novel low-frequency coded ground penetrating radar for deep detection. IEICE Electron. Expr. 2015, 12, 20150200. [CrossRef] 26. Golay, M.J.E. Complementary series. IEEE Trans. Inf. Theory 1961, 7, 82–87. [CrossRef] 27. Liu, X.; Yan, K.; Chen, Z.; Li, C.; Zhang, J.; Ye, S.; Fang, G. A m-sequence UWB radar system design and contrast test with an impulse radar. In Proceedings of the 17th International Conference on Ground Penetrating Radar (GPR), Rapperswil, Switzerland, 18–21 June 2018; pp. 1–4. [CrossRef] 28. Texas Instruments. Time Synchronization over the Air. Available online: http://software-dl.ti.com/simplelink/esd/simplelink_ cc13x0_sdk/3.20.00.23/exports/docs/proprietary-rf/proprietary-rf-users-guide/proprietary-rf/time-synchronization.html (accessed on 6 June 2020). 29. Novoselov, S.; Donskov, O. Distributed local positioning system using DWM1000 location chip. In Proceedings of the 4th International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T), Kharkov, Ukraine, 10–13 October 2017; pp. 489–492. [CrossRef] 30. Gu, Y.; Yang, B. Clock compensation two-way ranging (CC-TWR) based on ultra-wideband communication. In Proceedings of the 8th International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC), Harbin, China, 19–21 July 2018; pp. 1145–1150. [CrossRef] 31. Zhang, H.; Zhang, Z.; Gao, N.; Xiao, Y.; Meng, Z.; Li, Z. Cost-Effective Wearable Indoor Localization and Motion Analysis via the Integration of UWB and IMU. Sensors 2020, 20, 344. [CrossRef] [PubMed] 32. Sang, C.L.; Steinhagen, B.; Homburg, J.D.; Adams, M.; Hesse, M.; Rückert, U. Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods. Appl. Sci. 2020, 10, 3980. [CrossRef] 33. Liu, X.; Yan, K.; Yang, G.; Ye, S.; Fang, G. Improved vital signal extraction algorithm for through the wall radar. Electron. Lett. 2019, 56, 89–91. [CrossRef]

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Published: Jan 4, 2021

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