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Maneuvering target tracking is a challenging problem for sensor systemsbecause of the unpredictability of the targets' motions. This paper proposes anovel data-driven method for learning the dynamical motion model of a target.Non-parametric Gaussian process regression (GPR) is used to learn a target'snaturally shift invariant motion (NSIM) behavior, which is translationallyinvariant and does not need to be constantly updated as the target moves. Thelearned Gaussian processes (GPs) can be applied to track targets withindifferent surveillance regions from the surveillance region of the trainingdata by being incorporated into the particle filter (PF) implementation. Theperformance of our proposed approach is evaluated over different maneuveringscenarios by being compared with commonly used interacting multiple model(IMM)-PF methods and provides around $90\%$ performance improvement for amulti-target tracking (MTT) highly maneuvering scenario.

A Gaussian Process Regression based Dynamical Models Learning Algorithm for Target Tracking