Scalable multitarget tracking using multiple sensors: A belief propagation approach

We propose a method for multisensor-multitarget tracking with excellent scalability in the number of targets (which is assumed known), the number of sensors, and the number of measurements per sensor. Our method employs belief propagation based on a “detailed” factor graph that involves both target-related and measurement-related association variables. Using this approach, an increase in the number of targets, sensors, or measurements leads to additional variable nodes in the factor graph but not to higher dimensions of the messages. We observed very low runtimes of the proposed method; e.g., our MATLAB simulation of a scenario of 30 targets and 10 sensors without gating required less than one second per time step. The performance of the proposed method in terms of mean optimal subpattern assignment (OSPA) error compares well with that of state-of-the-art methods whose complexity scales exponentially with the number of targets. In particular, we observed that our method can outperform the sequential multisensor joint probabilistic data association filter (JPDAF) and performs similar to the Monte Carlo JPDAF.