Robust target tracking with quantized proximity sensors

As low energy, longevity and low cost transmitters are major requirements for wireless sensor networks (WSN), optimizing their design under energy constraints is of a great importance. To this goal and in order to solve the target tracking problem in WSN, we develop an algorithm to jointly, estimate the target position and optimize the quantization level where the transmitting power could be controlled by the sensors. At each sampling time, the adaptive algorithm provides not only the estimation of the target position by using the variational filtering (VF) but also, it gives the optimal quantization level. This optimal is obtained by minimizing the transmitting power under predicted Cramér-Rao bound constraint. Performance analysis shows that the proposed method outperforms both the VF algorithm with uniform quantization strategy and the VF algorithm based on binary sensors. Compared to the uniform quantization strategy, numerical examples show that energy saving up to 80% can be achieved when each sensor generates the same number of bits.