Adaptive sensor selection for target tracking using particle filter
- Author(s): Yazhao Wang 1
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View affiliations
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Affiliations:
1:
Department of Systems and Control, School of Mathematics and Systems Science, Beihang University (BUAA), Beijing 100191, People's Republic of China
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Affiliations:
1:
Department of Systems and Control, School of Mathematics and Systems Science, Beihang University (BUAA), Beijing 100191, People's Republic of China
- Source:
Volume 8, Issue 8,
October 2014,
p.
852 – 859
DOI: 10.1049/iet-spr.2013.0169 , Print ISSN 1751-9675, Online ISSN 1751-9683
This study presents a novel particle filtering approach for multiple sensor target tracking. In contrast to the standard form, each particle only uses the measurements received by a single selected sensor to estimate the mean and covariance of the target state. In order to do so, sensor selections are sampled using a particle filter and the hidden states are marginalised over. Finite number of sensors allows the exact calculation of the normalisation constant, thus the sampling can be done from the optimal importance distribution. In addition, an extension to the multiple sensor case of the probabilistic data association approach is also provided when clutter or false alarm is considered. Simulation examples that involve tracking a bearings-only target are provided to demonstrate the effectiveness of the proposed algorithms in critical situations where the single-sensor observability is lacking.
Inspec keywords: sensor fusion; clutter; covariance analysis; probability; adaptive filters; particle filtering (numerical methods); target tracking; direction-of-arrival estimation
Other keywords: probabilistic data association approach; adaptive sensor selection; normalisation constant; mean target state estimation; multiple sensor target tracking; particle flltering approach; bearings-only target; optimal importance distribution; clutter; covariance target state estimation
Subjects: Other topics in statistics; Signal processing theory; Filtering methods in signal processing; Other topics in statistics
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