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Hybrid probabilistic data association and variational filtering for multi-target tracking in wireless sensor networks

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Abstract

A hybrid signal processing scheme is proposed for distributed multi-target tracking (MTT). For the sake of resource efficiency in a wireless sensor network (WSN), we reduce the problem to parallel cluster-based single target tracking when the targets are far apart, and switch to MTT only when data association becomes ambiguous. A sequential monte carlo method is employed to assign the ambiguous observations to specific targets or clutter, based on association probabilities. Whereas the rest observations are incorporated by the variational filter, which approximates the distribution of involved particles by a simple Gaussian distribution for each target. The natural and adaptive message compression dramatically reduces the resource consumption of the WSN. The low computation complexity also guarantees the one-line execution of the hybrid MTT scheme. In addition, experimental results prove that the proposed scheme succeeds in distinguishing and tracking multiple targets even during the occlusions.
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hal-02319131 , version 1 (17-10-2019)

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Jing Teng, Hichem Snoussi, Cédric Richard, Yi Zhou. Hybrid probabilistic data association and variational filtering for multi-target tracking in wireless sensor networks. 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Dec 2009, Aruba, Caribbean Netherlands. pp.368-371, ⟨10.1109/CAMSAP.2009.5413254⟩. ⟨hal-02319131⟩
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