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Fast floor identification method based on confidence interval of Wi-Fi signals

Fast floor identification method based on confidence interval of Wi-Fi signals Abstract The indoor positioning technology is based on the hotpots of location based services (LBS). However, most indoor positioning systems are two-dimensional and couldn’t meet the requirements of today’s LBS. The complex indoor structures and environment determine the floor positioning rather than the altitude positioning in the vertical direction, so the floor identification is the key to three-dimensional indoor positioning systems. There are many restrictions for the existing floor identification methods based on barometer or inertial sensor. They need to get the comparable data in advance, or detect the test data changes in a certain period of time for accurate identification. The current floor identification methods based on ordinary Wi-Fi fingerprints are less effective in the complex environment. Therefore, a new floor identification method based on confidence interval of Wi-Fi signals was developed in this paper, which was divided into the offline stage and the online stage. In the offline stage, the dynamic Wi-Fi signal sequences were collected fast. Then, the adaptive partitioning of Wi-Fi signal intervals was carried out according to RSSI distribution characteristics in the multi-floor environment. Finally, the confidence levels were calculated and the database of fingerprints was constructed. In the online stage, the matching between the test fingerprints and those in the database was applied to obtain the confidence of APs on each floor monitored by the test fingerprints. The sums of the confidence of APs on each floor were calculated, and the floor corresponding to the maximum value was judged as the target floor. To verify the performance of the proposed method, it was compared with the majority voting committees, K-means, Naive Bayes and KNN methods. The results indicate that it was better than other methods in large complex indoor scenes. Its identification accuracy rate was 92.2% and the error rate was 7.8% only one floor away. Moreover, it also could significantly reduce the size of the fingerprint database and further improve the efficiency of algorithm. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png "Acta Geodaetica et Geophysica" Springer Journals

Fast floor identification method based on confidence interval of Wi-Fi signals

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References (43)

Publisher
Springer Journals
Copyright
2019 Akadémiai Kiadó
ISSN
2213-5812
eISSN
2213-5820
DOI
10.1007/s40328-019-00264-6
Publisher site
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Abstract

Abstract The indoor positioning technology is based on the hotpots of location based services (LBS). However, most indoor positioning systems are two-dimensional and couldn’t meet the requirements of today’s LBS. The complex indoor structures and environment determine the floor positioning rather than the altitude positioning in the vertical direction, so the floor identification is the key to three-dimensional indoor positioning systems. There are many restrictions for the existing floor identification methods based on barometer or inertial sensor. They need to get the comparable data in advance, or detect the test data changes in a certain period of time for accurate identification. The current floor identification methods based on ordinary Wi-Fi fingerprints are less effective in the complex environment. Therefore, a new floor identification method based on confidence interval of Wi-Fi signals was developed in this paper, which was divided into the offline stage and the online stage. In the offline stage, the dynamic Wi-Fi signal sequences were collected fast. Then, the adaptive partitioning of Wi-Fi signal intervals was carried out according to RSSI distribution characteristics in the multi-floor environment. Finally, the confidence levels were calculated and the database of fingerprints was constructed. In the online stage, the matching between the test fingerprints and those in the database was applied to obtain the confidence of APs on each floor monitored by the test fingerprints. The sums of the confidence of APs on each floor were calculated, and the floor corresponding to the maximum value was judged as the target floor. To verify the performance of the proposed method, it was compared with the majority voting committees, K-means, Naive Bayes and KNN methods. The results indicate that it was better than other methods in large complex indoor scenes. Its identification accuracy rate was 92.2% and the error rate was 7.8% only one floor away. Moreover, it also could significantly reduce the size of the fingerprint database and further improve the efficiency of algorithm.

Journal

"Acta Geodaetica et Geophysica" – Springer Journals

Published: Sep 1, 2019

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