Message Passing Algorithms for Scalable Multitarget Tracking

Situation-aware technologies enabled by multitarget tracking will lead to new services and applications in fields such as autonomous driving, indoor localization, robotic networks, and crowd counting. In this tutorial paper, we advocate a recently proposed paradigm for scalable multitarget tracking that is based on message passing or, more concretely, the loopy sum-product algorithm. This approach has advantages regarding estimation accuracy, computational complexity, and implementation flexibility. Most importantly, it provides a highly effective, efficient, and scalable solution to the probabilistic data association problem, a major challenge in multitarget tracking. This fact makes it attractive for emerging applications requiring real-time operation on resource-limited devices. In addition, the message passing approach is intuitively appealing and suited to nonlinear and non-Gaussian models. We present message-passing-based multitarget tracking methods for single-sensor and multiple-sensor scenarios, and for a known and unknown number of targets. The presented methods can cope with clutter, missed detections, and an unknown association between targets and measurements. We also discuss the integration of message-passing-based probabilistic data association into existing multitarget tracking methods. The superior performance, low complexity, and attractive scaling properties of the presented methods are verified numerically. In addition to simulated data, we use measured data captured by two radar stations with overlapping fields-of-view observing a large number of targets simultaneously.