Adaptive quantized target tracking in wireless sensor networks

This paper addresses target tracking in wireless sensor networks (WSN) where the observed system is assumed to evolve according to a probabilistic state space model. We propose to improve the use of the variational filtering (VF) by optimally quantizing the data collected by the sensors. Recently, VF has been proved to be suitable to the communication constraints of WSN. Its efficiency relies on the fact that the online update of the filtering distribution and its compression are executed simultaneously. However, this problem has been used only for binary sensor networks neglecting the transmission energy consumption in a WSN and the information relevance of sensor measurements. Our proposed method is intended to jointly estimate the target position and optimize the quantization level under fixed and variable transmitting power. At each sampling instant, the adaptive method provides not only the estimate of the target position by using the VF but gives also the optimal number of quantization bits per observation. The adaptive quantization is achieved by minimizing the predicted Cramér–Rao bound if the transmitting power is constant for all sensors, and optimizing the power scheduling under distortion constraint if this power is variable. The computation of the predicted Cramér–Rao bound is based on the target position predictive distribution provided by the VF algorithm. The proposed adaptive quantization scheme suggests that the sensors with bad channels or poor observation qualities should decrease their quantization resolutions or simply become inactive in order to save energy.