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.


    Access

    Download


    Export, share and cite



    Title :

    A Learning Gaussian Process Approach for Maneuvering Target Tracking and Smoothing


    Contributors:


    Publication date :

    2021-02-01


    Size :

    1739738 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Maneuvering target tracking with non-Gaussian noise

    Song Xiaoquan / Sun Zhongkang | IEEE | 1997


    Maneuvering target tracking via smoothing and filtering through measurement concatenation

    CLOUTIER, JAMES R. / LIN, CHING-FANG / YANG, CHUN | AIAA | 1993



    Maneuvering target tracking via smoothing and filtering through measurement concatenation

    CLOUTIER, JAMES / LIN, CHING-FANG / YANG, CHUN | AIAA | 1991


    IMM Forward Filtering and Backward Smoothing for Maneuvering Target Tracking

    Nadarajah, N. / Tharmarasa, R. / McDonald, M. et al. | IEEE | 2012