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Data-driven online variational filtering in wireless sensor networks

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Abstract

In this paper, a data-driven extension of the variational algorithm is proposed. Based on a few selected sensors, target tracking is performed distributively without any information about the observation model. Tracking under such conditions is possible if one exploits the information collected from extra inter-sensor RSSI measurements. The target tracking problem is formulated as a kernel matrix completion problem. A probabilistic kernel regression is then proposed that yields a Gaussian likelihood function. The likelihood is used to derive an efficient and accelerated version of the variational filter without resorting to Monte Carlo integration. The proposed data-driven algorithm is, by construction, robust to observation model deviations and adapted to non-stationary environments.
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Dates and versions

hal-02307462 , version 1 (07-10-2019)

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Hichem Snoussi, Jean-Yves Tourneret, Petar Djuric, Cédric Richard. Data-driven online variational filtering in wireless sensor networks. ICASSP 2009 - 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, Apr 2009, Taipei, Taiwan. pp.2413-2416, ⟨10.1109/ICASSP.2009.4960108⟩. ⟨hal-02307462⟩
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