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Model-based approaches for target tracking and smoothing estimate the infinite number of possible target trajectories using a finite set of models. This article proposes a data-driven approach that represents the possible target trajectories using a distribution over an infinite number of functions. Recursive Gaussian process, and derivative-based Gaussian process approaches for target tracking, and smoothing are developed, with online training, and parameter learning. The performance evaluation over two highly maneuvering scenarios, shows that the proposed approach provides 80 and 62% performance improvement in the position, and 49 and 22% in the velocity estimation, respectively, as compared to the best model-based filter.

A Learning Gaussian Process Approach for Maneuvering Target Tracking and Smoothing