Adaptive Threshold Selection for Collaborative Target Tracking

A uniform threshold is not reasonable as a faraway sensor can hardly get the same signal strength as that of a near one from the target. To cope with this problem, we give an adaptive threshold selection (ATS) algorithm, in which we construct the minimum error probability cost function based on the probability of false positive and false negative of sensor nodes, and calculate the optimal detection threshold to select the nodes that attend in target tracking. The simulation results show that the proposed algorithm improves the tracking accuracy while reducing the network energy consumption.