Unscented Kalman Filtering based quantized innovation fusion for target tracking in WSN with feedback

The quantized innovation fusion approach to tracking a target with nonlinear Gaussian dynamics in wireless sensor network (WSN) is proposed. A hierarchical innovation fusion structure with feedback from the fusion center (FC) to each deployed sensor is proposed. The measurement innovation in each local sensor node is quantized and then transmitted to the FC. Then the FC estimates the state of the target using the unscented Kalman filtering (UKF) strategy. 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 example is given to illustrate the proposed scheme obtains average percentage of communication energy saving up to 41.5% compared with the uniform quantization, while keeping tracking accuracy very closely to the clairvoyant UKF that relies on analog-amplitude measurements.