Probabilistic Track-to-Track Association

We consider the track-to-track association problem. This problem is often a key ingredient when seeking to integrate data from multiple sensors. We propose a probabilistic approach, inspired by the joint probabilistic data association, or JPDA, approach used in the data association problem. To solve the proposed model we adapt a recent deterministic polynomial-time approximation algorithm. We give consideration also to the setting in which one or more sensors may contain biases.