Optimal data compression for multisensor target tracking with communication constraints

Target tracking using multiple sensors is one important application in military surveillance and industrial sensing. Due to the communication constraints between each sensor and the data processor that estimates the target state using all the data from multiple sensors, it is crucial for each sensor to compress its measurements optimally so that the data processor can estimate the target state with minimum mean square error. We limit the data compression at each sensor to be a linear transform that reduces the measurement dimension. We use the results by Zhang et al. (2003) to do measurement compression and obtain the optimal linear transform matrix for each sensor based on steady state analysis. To activate or remove a sensor dynamically, we consider a sequential update scheme to modify the data compression matrix for each sensor with an arbitrary dimensional requirement due to the communication constraint. We compare our approach with traditional centralized and distributed tracking schemes and indicate the advantages of using sensor data compression for tracking in a sensor network environment. Simulation results with three sensors show that the estimation accuracy of the proposed scheme is very close to that of the centralized estimator.