Integrating Covariance Intersection Into Bayesian Multitarget Tracking Filters

Multitarget 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 mo...

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Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems Vol. 59; no. 2; pp. 1382 - 1391
Main Authors: Clark, Daniel E., Campbell, Mark A.
Format: Journal Article
Language:English
Published: New York IEEE 01.04.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Bayes methods
Bayesian analysis
Computer networks
Covariance
Data integration
Estimates
Estimation
Kalman filters
Multiple target tracking
Radar tracking
Random variables
Robot sensing systems
Target tracking
Tracking filters
Tracking systems
ISSN:0018-9251, 1557-9603
Online Access:Get full text
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Summary:Multitarget 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 article, we develop a Bayesian multitarget estimator based on the covariance intersection algorithm for multitarget track-to-track data fusion. The approach is integrated into a multitarget 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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ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2022.3201509