Quantized Measurement Fusion for Target Tracking in Wireless Sensor Networks

The quantized measurement fusion problem for target tracking in wireless sensor networks (WSNs) is investigated. Due to the limited energy and bandwidth, each activated node quantizes and then transmits the local measurements by probabilistic quantization strategy. The fusion center (FC) estimates the target state in a dimension compression way instead of merging all the quantized messages to a vector (augmented scheme). In this paper, focuses are on tradeoff between energy consumption and the global tracking accuracy. A closed-form solution to the optimization problem for bandwidth scheduling is given, where the total energy consumption measure is minimized subject to a constraint on the mean square error (MSE) incurred by quasi-best linear unbiased estimation (Quasi-BLUE) fusion. Nonlinear Gaussian discrete-time system model following the Sigma-point Kalman Filtering (SPKF) principle is employed. Simulation example illustrates the proposed scheme obtains average percentage of communication energy saving up to 44.9% compared with the uniform quantization, while keeps computational burden reduction 12% compared with the augmented scheme.