Multisensory Fusion Algorithms for Tracking
Lucy Y. Pao, Sean D. O'Neil · 2005
In this paper we extend a multitarget tracking algorithm for use in multisensory tracking situations. The algorithm we consider is Joint Probabilistic Data Association (JPDA). JPDA is extended to handle an arbitrary number of sensors under the assumption that the sensor measurement errors are independent across sensors. We also show how filtering can be handled in multisensory JPDA (MSJ PDA) without leading to an exponential increase in filtering complexity. Simulation results are presented comparing the performance of the MSJPDA with another multisensory fusion algorithm and with the single-sensor JPDA algorithm.