Scalable adaptive multitarget tracking using multiple sensors

Abstract

In networked mobile multitarget tracking systems, parameters such as detection probabilities, clutter rates, and motion model parameters are often unknown and time-varying. Such parameter variability can seriously degrade the performance of a multitarget tracking system. Here, we propose a Bayesian tracking framework in which the multisensor-multitarget tracking problem is formulated according to the measurement origin uncertainty paradigm and the unknown parameters-in the present case, the detection probabilities at the individual sensors-are modeled as Markov chains. The resulting Bayesian estimation problem is then solved using the belief propagation scheme. This approach results in a multisensormultitarget tracking method that is able to adapt to the time variations of the detection probabilities. Moreover, the method has a low complexity that scales very well in all relevant system parameters. The performance of the method is assessed using data collected by a mobile underwater wireless sensor network.