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ICASSP
2009
IEEE

Data-driven online variational filtering in wireless sensor networks

8 years 10 months ago
Data-driven online variational filtering in wireless sensor networks
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.
Hichem Snoussi, Jean-Yves Tourneret, Petar M. Djur
Added 21 May 2010
Updated 21 May 2010
Type Conference
Year 2009
Where ICASSP
Authors Hichem Snoussi, Jean-Yves Tourneret, Petar M. Djuric, Cédric Richard
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