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Extended target tracking estimates the centroid and shape of the target inspace and time. In various situations where extended target tracking isapplicable, the presence of multiple targets can lead to interference,particularly when they maneuver behind one another in a sensor like a camera.Nonetheless, when dealing with multiple extended targets, there's a tendencyfor them to share similar shapes within a group, which can enhance theirdetectability. For instance, the coordinated movement of a cluster of aerialvehicles might cause radar misdetections during their convergence ordivergence. Similarly, in the context of a self-driving car, lane markingsmight split or converge, resulting in inaccurate lane tracking detections. Awell-known joint probabilistic data association coupled (JPDAC) filter canaddress this problem in only a single-point target tracking. A variation ofJPDACF was developed by introducing a nonparametric Spatio-Temporal JointProbabilistic Data Association Coupled Filter (ST-JPDACF) to address theproblem for extended targets. Using different kernel functions, we manage thedependency of measurements in space (inside a frame) and time (between frames).Kernel functions are able to be learned using a limited number of trainingdata. This extension can be used for tracking the shape and dynamics ofnonparametric dependent extended targets in clutter when targets sharemeasurements. The proposed algorithm was compared with other well-knownsupervised methods in the interfering case and achieved promising results.

Nonparametric Spatio-Temporal Joint Probabilistic Data Association Coupled Filter and Interfering Extended Target Tracking