Adaptive kernel Kalman filter based belief propagation algorithm for maneuvering multi-target tracking

Sun, Mengwei and Davies, Mike E. and Proudler, Ian K. and Hopgood, James R. (2022) Adaptive kernel Kalman filter based belief propagation algorithm for maneuvering multi-target tracking. IEEE Signal Processing Letters, 29. pp. 1452-1456. ISSN 1070-9908 (https://doi.org/10.1109/lsp.2022.3184534)

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Abstract

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.