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.
Tim Zajic, Tim Zajic,
"Probabilistic track-to-track association", Proc. SPIE 9474, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV, 94740B (21 May 2015); doi: 10.1117/12.2177206; https://doi.org/10.1117/12.2177206