A Novel Approach of Sensor Management Based on Compressive Sensing in Sensor Network

Sensor management is more and more important in simultaneous tracking and identification with multiple sensors. In this paper, we proposed a novel sensor management approach based on compressed sensing for target tracking in sensor networks, to determine the subset of sensors with the most informative data. With the proposed approach, we consider original sensor management problem as a constrained optimization problem, where the goal is to determine the optimal values of probabilities so that the trace of the Fisher information matrix at any given time step can get its maximum. We also derived the formulations for the Fisher information matrix in case that the fusion center has complete measurement sparsity information. Experimental results show that the proposed approach consume fewer energy than the optimal scenario in which all sensor observations are transmitted to the fusion center.