Multiple-Target Tracking in Dense, Noisy Environments: A Probabilistic Mapping Perspective

Abstract

A new approach is taken to address the various aspects of the multiple-target tracking (MTT) problem in dense and noisy environments. Instead of fixing the trackers on the potential targets as the conventional tracking algorithms do, this new approach is fundamentally different in that an array of parallel-distributed trackers is laid in the search space. The difficult data-track association problem that has challenged the conventional trackers becomes a non-issue with this new approach. By partitioning the search space into cells, this new approach, called PMAP (probabilistic Mapping), dynamically calculates the spatial probability distribution of targets in the search space via Bayesian updates. The distribution is spread at each time step, following some fairly general Markov-chain target motion model, to become the prior probabilities of the next scan. This framework can effectively handle data from multiple sensors and incorporates contextual information, such as terrain and weather, by performing a form of Evidential Reasoning. Used as a pre-filtering device, the PMAP is shown to remove noise-like false alarms effectively, while keeping target dropout rate very low. This gives the downstream track linker a much easier job to perform. A related benefit is that with PMAP it is now possible to lower the detection threshold and to enjoy high Probability of Detection and low Probability of False Alarm at the same time, thereby improving overall tracking performance. The feasibility of using PMAP to track specific targets in an end-game scenario is also discussed. Both real and simulated data are used to illustrate the PMAP performance. Some related applications based on the PMAP approach, including a spatial-temporal sensor data fusion application, are mentioned.