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Integrating covariance intersection into Bayesian multitarget tracking filters

Integrating covariance intersection into Bayesian multitarget tracking filters
Integrating covariance intersection into Bayesian multitarget tracking filters
Multi-target tracking systems typically provide sets of estimated target states as their output. It is challenging to be able to integrate these outputs as inputs to other tracking systems to gain a better picture of the area under surveillance since they do not conform to the standard observation model. Moreover, in cyclic distributed systems, there may be common information between state estimates that would mean that fused estimates may become overconfident and corrupt the system. In this paper we develop a Bayesian multi-target estimator based on the covariance intersection algorithm for multi-target track-to-track data fusion. The approach is integrated into a multi-target tracking algorithm and demonstrated in simulations. The approach is able to account for missed tracks and false tracks produced by another tracking system.
Bayes methods, Estimation, Kalman filters, Radar tracking, Random variables, Robot sensing systems, Target tracking
0018-9251
1382-1391
Clark, Daniel E.
537f80e8-cbe6-41eb-b1d4-31af1f0e6393
Campbell, Mark A.
88f9da10-00c8-4060-ae81-877399b61908
Clark, Daniel E.
537f80e8-cbe6-41eb-b1d4-31af1f0e6393
Campbell, Mark A.
88f9da10-00c8-4060-ae81-877399b61908

Clark, Daniel E. and Campbell, Mark A. (2022) Integrating covariance intersection into Bayesian multitarget tracking filters. IEEE Transactions on Aerospace and Electronic Systems, 59 (2), 1382-1391. (doi:10.1109/TAES.2022.3201509).

Record type: Article

Abstract

Multi-target tracking systems typically provide sets of estimated target states as their output. It is challenging to be able to integrate these outputs as inputs to other tracking systems to gain a better picture of the area under surveillance since they do not conform to the standard observation model. Moreover, in cyclic distributed systems, there may be common information between state estimates that would mean that fused estimates may become overconfident and corrupt the system. In this paper we develop a Bayesian multi-target estimator based on the covariance intersection algorithm for multi-target track-to-track data fusion. The approach is integrated into a multi-target tracking algorithm and demonstrated in simulations. The approach is able to account for missed tracks and false tracks produced by another tracking system.

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Chernoff_Fusion_rev_49_ - Accepted Manuscript
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More information

e-pub ahead of print date: 25 August 2022
Keywords: Bayes methods, Estimation, Kalman filters, Radar tracking, Random variables, Robot sensing systems, Target tracking

Identifiers

Local EPrints ID: 475490
URI: http://eprints.soton.ac.uk/id/eprint/475490
ISSN: 0018-9251
PURE UUID: cad91a38-3e4a-4c7a-9973-46ea449ba99c

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Date deposited: 20 Mar 2023 17:42
Last modified: 16 Mar 2024 23:15

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Contributors

Author: Daniel E. Clark
Author: Mark A. Campbell

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