Fusion of Sensor Measurements and Target-Provided Information in Multitarget Tracking

Tracking multiple time-varying states based on heterogeneous observations is a key problem in many applications. Here, we develop a statistical model and algorithm for tracking an unknown number of targets based on the probabilistic fusion of observations from two classes of data sources. The first class, referred to as target-independent perception systems (TIPSs), consists of sensors that periodically produce noisy measurements of targets without requiring target cooperation. The second class, referred to as target-dependent reporting systems (TDRSs), relies on cooperative targets that report noisy measurements of their state and their identity. We present a joint TIPS-TDRS observation model that accounts for observation-origin uncertainty, missed detections, false alarms, and asynchronicity. We then establish a factor graph that represents this observation model along with a state evolution model including target identities. Finally, by executing the sum-product algorithm on that factor graph, we obtain a scalable multitarget tracking algorithm with inherent TIPS-TDRS fusion. The performance of the proposed algorithm is evaluated using simulated data as well as real data from a maritime surveillance experiment.

Automated summary

Tracking multiple time-varying states based on heterogeneous observations is addressed in this paper. A statistical model and algorithm are developed for tracking an unknown number of targets by combining observations from two classes of data sources. The proposed algorithm incorporates factors such as observation-origin uncertainty, missed detections, false alarms, and asynchronicity. By applying the sum-product algorithm on a factor graph, a scalable multitarget tracking algorithm with inherent fusion capability is obtained. The algorithm's performance is evaluated using simulated and real data from a maritime surveillance experiment.