Tracking an unknown number of targets using multiple sensors: A belief propagation method

We propose a multisensor method for tracking an unknown number of targets. Low computational complexity and very good scalability in the number of targets, number of sensors, and number of measurements per sensor are achieved by running a belief propagation (BP) message passing scheme on a suitably devised factor graph. Using a redundant formulation of data association uncertainty and “augmented target states” including target indicators allows the proposed BP method to leverage statistical independencies for a drastic reduction of complexity. The proposed method is shown to outperform previously proposed multisensor methods for multitarget tracking, including methods with a less favorable scaling behavior.