Adaptive Kernel Kalman Filter Based Belief Propagation Algorithm for Maneuvering Multi-Target Tracking

This letter incorporates the adaptive kernel Kalman filter (AKKF) into the belief propagation (BP) algorithm for multi-target tracking (MTT) in single-sensor systems. The algorithm is capable of tracking an unknown and time-varying number of targets, in the presence of false alarms, clutter and meas...

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Bibliographic Details
Published in:IEEE signal processing letters 2022, Vol.29, p.1-5
Main Authors: Sun, Mengwei, Davies, Mike E., Proudler, Ian K., Hopgood, James R.
Format: Article
Language:English
Subjects:
Adaptive kernel Kalman filter
Algorithms
Belief propagation
Clutter
Covariance matrices
Data association
False alarms
Kalman filters
Kernel
Kernels
Multi-target tracking
Multiple target tracking
Probability density function
Propagation
Proposals
Signal processing algorithms
Target tracking
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Summary:This letter incorporates the adaptive kernel Kalman filter (AKKF) into the belief propagation (BP) algorithm for multi-target tracking (MTT) in single-sensor systems. The algorithm is capable of tracking an unknown and time-varying number of targets, in the presence of false alarms, clutter and measurement-to-target association uncertainty. Experiment results reveal that the proposed method has a favourable tracking performance using the generalized optimal sub-patten assignment (GOSAP) metrics at substantially less computation cost than the particle filter (PF) based multi-target tracking (MTT) BP algorithm.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2022.3184534