Collaborative target tracking in wireless sensor networks using quantized innovations and Sigma-Point Kalman Filtering

Abstract

The decentralized collaborative target tracking problem in wireless sensor network (WSN) is investigated in the fusion of quantized innovations perspective. A hierarchical fusion structure with feedback from the fusion center (FC) to each deployed sensor is proposed for tracking a target with nonlinear Gaussian dynamics. Probabilistic quantization strategy is employed in the local sensor node to quantize the innovation. After the FC received the quantized innovations, it estimates the state of the target using the sigma-point kalman filtering (SPKF). To attack the energy/power source and communication bandwidth constraints, we consider the tradeoff between the communication energy 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 covariance of the quantization noises. Simulation results illustrate that the proposed scheme obtains average percentage of communication energy saving up to 40.8% compared with the uniform quantization, while keeping tracking accuracy very closely to the clairvoyant SPKF even when the latter relies on analog-amplitude measurements.