Improved adaptive unscented Kalman filter algorithm for target tracking

An adaptive unscented Kalman filter (AUKF) algorithm is proposed to solve the problem that the statistical characteristics of the process noise are unknown in the target tracking, which leads to filter divergence or low filtering precision. The improved Sage-Husa estimator is used to estimate the st...

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Bibliographic Details
Published inMATEC Web of Conferences Vol. 139; p. 186
Main Authors Han, Chunyao, Xiong, Jiajun, Zhang, Kai
Format Journal Article Conference Proceeding
LanguageEnglish
Published Les Ulis EDP Sciences 01.01.2017
Subjects
Adaptive algorithms
Adaptive filters
adaptive unscented Kalman filter
Algorithms
arget tracking
Computer simulation
Divergence
Filtration
Kalman filters
motion model
Noise
Numerical stability
State estimation
Statistical analysis
time-varying process noise statistic estimator
Tracking
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Summary:An adaptive unscented Kalman filter (AUKF) algorithm is proposed to solve the problem that the statistical characteristics of the process noise are unknown in the target tracking, which leads to filter divergence or low filtering precision. The improved Sage-Husa estimator is used to estimate the statistical characteristics of the unknown process noise in the filtering process, and to judge and suppress the filtering divergence, which effectively improves the numerical stability of the filtering and reduces the error of the state estimation. The simulation results show that the improved AUKF algorithm not only keeps convergence but also improves the accuracy and stability of the target tracking under the condition of unknown time-varying process noise statistic, compared with the standard UKF algorithm.
ISSN:2261-236X
2274-7214
2261-236X
DOI:10.1051/matecconf/201713900186