Weighted Average Approach to Quantized Measurement Fusion in Wireless Sensor Network

The quantized measurement fusion problem for target tracking in a sensor network is investigated using a weighted average approach. The measurement in each local sensor is quantized by uniform quantization and then transmitted to a fusion center (FC). To estimate the state of the target in the FC, the quantized messages are first to be combined in a weighted average way instead of merging all the quantized messages to a vector. Then extended Kalman filtering (EKF) is employed to estimate the target state. Focuses are on tradeoff between bandwidth of each sensor and the global tracking accuracy. The closed-form solution to the optimization problem for bandwidth scheduling is given, where the mean square error (MSE) incurred by weighted average fusion is minimized subject to a constraint on the total energy consumption. Nonlinear Gaussian discrete-time system model following the EKF principle is employed. Simulation example is given to illustrate the proposed scheme can obtain average percentage of energy saving up to 37.2% with computational burden reduction 32%.