Effects of operation parameters on multitarget tracking in proximity sensor networks

This paper investigates effects of operation parameters on multitarget tracking in proximity sensor networks. In such a network, the sensors report a detection when a target is within the proximity; otherwise, the sensors report no detection. Previous work has revealed the potential of multitarget tracking via the particle-based probability hypothesis density (PHD) filter when incorporating these binary reports. This work investigates how the sensor density, sensing range, and target separation affect the ability of the PHD filter to estimate the number of targets in the scene and to localize these targets (as measured by four different metrics). Two possible measurement models are considered. The disc model assumes target detection within a sensing radius, and the probabilistic model assumes 1/rα propagation decay of the source signal so that the probability of detection decreases with range r. The simulations demonstrate the simplistic disc model is inadequate for the PHD filter to estimate the number of targets, and the filter for the disc model exhibits difficulty to localize widely separated targets for low sensor densities. On the other hand, the more realistic probabilistic model leads to a PHD filter that can accurately estimate the number and locations of targets even for small target separations.