Instrumenting wireless sensor networks for real-time surveillance

This paper considers the problem of tracking an unknown number of targets using a wireless sensor network for surveillance. In particular, we consider the case in which each sensor reports only a binary value indicating whether an object is detected near the reporting sensor or not. Since the number of targets and initial states of targets are unknown in advance, the task of tracking with coarse measurements from binary sensors is extremely challenging. This paper develops an efficient multi-sensor fusion algorithm which converts binary detections into finer position reports using spatial correlation. The fused measurements are then used by the Markov chain Monte Carlo data association (MCMCDA) algorithm to track an unknown number of targets. The algorithm has been successfully applied in real-time to track an unknown number of human subjects moving through an outdoor field monitored by a wireless sensor network. To our knowledge, this paper presents the first large-scale demonstration of multi-target tracking using a wireless sensor network without relying on classification.