Data-driven online variational filtering in wireless sensor networks

Hichem Snoussi, Jean-Yves Tourneret, Petar M. Djuric, Cedric Richard
2009 2009 IEEE International Conference on Acoustics, Speech and Signal Processing  
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
more » ... ood 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. Index Terms-Bayesian filtering, sensor networks, machine learning.
doi:10.1109/icassp.2009.4960108 dblp:conf/icassp/SnoussiTDR09 fatcat:jj6alrg6g5f5xg5voi6qhn5yuy