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A nonlinear estimation for target tracking in wireless sensor networks using quantized variational filtering

Abstract : We consider the problem of target tracking in wireless sensor networks where the nonlinear observed system is assumed to progress respecting to a probabilistic state space model. This proposition improves the use of the variational filtering (VF) by jointly estimating the target position and optimizing the power scheduling, where the sensor observations are corrupted by additive noises and attenuated by path-loss coefficient. In fact, the quantized variational filtering (QVF) has been shown to be adapted to the communication constraints of sensor networks. Its efficiency relies on the fact that the online update of the filtering distribution and its compression are executed simultaneously. We first optimize quantization for reconstructing a single sensors measurement, and developing the optimal number of quantization levels as well as the minimal power transmitted by sensors under distortion constraint. Then we estimate the path-loss coefficient by maximizing the a posteriori distribution and the target position by using the QVF. The simulation results prove that the adaptive power optimization algorithm, outperforms both the QVF algorithm using uniform power level and the VF algorithm based on binary sensors.
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Contributor : Jean-Baptiste Vu Van Connect in order to contact the contributor
Submitted on : Tuesday, September 17, 2019 - 3:33:53 PM
Last modification on : Friday, August 27, 2021 - 3:14:06 PM


  • HAL Id : hal-02290327, version 1




Majdi Mansouri, Hichem Snoussi, Cédric Richard. A nonlinear estimation for target tracking in wireless sensor networks using quantized variational filtering. 2009 3rd International Conference on Signals, Circuits and Systems (SCS 2009), Nov 2009, Medenine, Tunisia. pp.1-4. ⟨hal-02290327⟩



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