Multiple target detection and tracking with a sensor network

An algorithm is developed for joint tracking and detection of multiple maneuvering targets using a wireless sensor network. The target existence probability framework is adopted in which a collection of tentative tracks, each characterised by a posterior density and existence probability, is maintained. Track state posterior densities are approximated using the unscented Kalman filter and the interacting multiple model algorithm. The advantage of this approach compared to particle filter-based approaches is that it enables more computationally efficient tracking of multiple targets. The performance of the algorithm is examined as a function of signal-to-noise ratio and the number of bits per observation for a scenario involving three maneuvering targets. Good performance is achieved in all cases considered.