Joint adaptive quantization and fading channel estimation for target tracking in wireless sensor networks

We consider the problem of target tracking in wireless sensor networks where the observed system is assumed to progress respecting to a probabilistic state space model. We propose to improve the use of the quantized variational filtering (QVF) by jointly optimize the quantization level and estimate the path-loss between sensors. Recently, quantized variational filtering QVF has been proved 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. Our proposed technique is developed to jointly optimize the quantization level and estimate the path-loss coefficient, where the sensors are connected with unknown fading channels. First, sensors observations are quantized under a constant transmitting power constraint. This quantization is performed by online maximizing the predictive Fisher Information (FI). Then, we estimate the path-loss coefficient by maximizing its a posteriori distribution. The simulation results show that the joint adaptive quantization and fading channel estimation algorithm, for the same sensor transmitting power, outperforms both the VF algorithm using a fixed optimal quantization level and the VF algorithm based on binary sensors.