Abstract
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.
Highlights
Multiple target tracking [1], [2] includes state estimation of the targets of interest from a set of noisy measurements and false alarms
We propose a Recursive Gaussian Process Motion Tracker (RGP MT)1 for online model-free point target tracking
Μkf = μkf−1 + Gk zk − μkf where u denotes the input basis vector, ukf represents the input vector corresponding to the kth measurement vector zk, Jk and Bk are matrices derived from the Gaussian process (GP) regression (3) and (4), respectively, ̃ ̇ andrepresent, respectively, the predicted and filtered variables, μ f and C f are, respectively, the sparse GP mean and covariance of the modeled nonlinear function f at u, Gk is the gain matrix, σ 2 is the measurement noise variance hyperparameter, μf and Cf represent, respectively, the estimated mean and covariance of the unknown function evaluated at the measured location vector ukf
Summary
Multiple target tracking [1], [2] includes state estimation of the targets of interest from a set of noisy measurements and false alarms. Two major processes involved in point target tracking are the data association, i.e., measurement to target/track assignment, and the state estimation, which includes target state update using the assigned measurement [5]. The performance of these two processes is interdependent. The focus of this article is in real-time estimation only and for the first time, an online data driven approach for point target tracking and fixed-lag smoothing is proposed. IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL. 57, NO. 1 FEBRUARY 2021
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