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M.S. Grewal, A.D. Andrews
Kalman Filtering Theory and Practice Using MATLAB
S. Changey, D. Beauvois, V. Fleck (2005)
Static and dynamic attitude decomposition for estimation with magnetometer sensorIFAC Proceedings Volumes, 38
E. Wan, Rudolph Merwe (2000)
The unscented Kalman filter for nonlinear estimationProceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373)
A. Balakrishnan (1984)
Kalman Filtering Theory
Xiangdong Lin, T. Kirubarajan, Y. Bar-Shalom, S. Maskell (2002)
Comparison of EKF, pseudomeasurement, and particle filters for a bearing-only target tracking problem, 4728
D. Carter, Aaron Brown (2006)
Algorithms for Geolocation of an Ad Hoc Network of Unmanned SystemsJournal of Guidance Control and Dynamics, 29
M. Vemula, M. Bugallo, P. Djurić (2007)
Performance Comparison of Gaussian-Based Filters Using Information MeasuresIEEE Signal Processing Letters, 14
B. Ristic, S. Arulampalam, N. Gordon (2004)
Beyond the Kalman Filter: Particle Filters for Tracking Applications
Y. Bar‐Shalom, X.R. Li, T. Kirubarajan
Estimation with Applications to Tracking and Navigation
J.J. Jr LaViola
A comparison of unscented and EKF ing for estimating quarternion motion
S. Julier, J. Uhlmann (1997)
New extension of the Kalman filter to nonlinear systems, 3068
Y. Bar-Shalom, Xiaorong Li, T. Kirubarajan (2001)
Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software
X. Lin, T. Kirubarajan, Y. Bar‐shalom, S. Maskell
Comparison of EKF, pseudomeasurement filter and particle filter for a bearing‐only tracking problem
N. Cui, L. Hong, J. Layne (2005)
A comparison of nonlinear filtering approaches with an application to ground target trackingSignal Process., 85
S. Julier, J. Uhlmann (2004)
Unscented filtering and nonlinear estimationProceedings of the IEEE, 92
J. Laviola (2003)
A comparison of unscented and extended Kalman filtering for estimating quaternion motionProceedings of the 2003 American Control Conference, 2003., 3
E.A. Wan, R. van der Merwe
Kalman Filtering and Neural Networks, Chapter 7: The Unscented Kalman Filter
Purpose – The purpose of this paper is to provide an analysis on using two non‐conventional nonlinear estimating filters compared to the traditional linearized extended Kalman filter (EKF). This analysis will look at two state‐of‐the‐art applications and will provide insight to the problems associated with these applications. Design/methodology/approach – The approach taken was to simulate both applications with three different filter designs: EKF, unscented Kalman filter, and particle filter. After results and explanations are given for both applications, then there is a comparison of results between the two applications to compare and contrast their findings. Findings – This research shows how critical it is when selecting a filter for different applications. Not only is tuning the filter properly a necessity, but choosing a filter that is optimum for the application also greatly affects the accuracy and precision of the results. Research limitations/implications – As these filter methods are proven feasible for these applications, testing can move beyond simulation. Further research could compare other nonlinear filters to these results to determine if a better estimation technique exists. Practical implications – This paper shows a lot of the issues one must face when choosing an estimation technique for their application as well as the impact the technique can have on the outcome. Originality/value – This paper clearly describes the decision‐making criteria in regards to these two specific applications. These two applications are current technological problems that many are trying to solve. This paper shows where and why errors in calculations occur. It also offers insight into different ways to solve these problems when the specific application is taken into account.
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering – Emerald Publishing
Published: Mar 6, 2009
Keywords: Simulation; Programming and algorithmic theory; Estimation
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