To address the problem of extended target tracking (ETT) with measurement-origin uncertainty in a multipath environment with terrain-constrained motion model, a new generalized version of the standard probabilistic data association (PDA) filter, called MP-ET-PDA, based on random matrices (RM) is proposed in this paper. In the MP-ET-PDA filter, we assume that multipath detections and clutter are possible in the extended target tracking problem, which are prevalent in practical systems but barely addressed in the literature. Further, a clustering-aided MP-ET-PDA algorithm with a reduced computational complexity that makes use of the Variational Bayesian (VB) technique, called MP-ET-PDA-VB, is presented to provide near real-time processing capability in ETT problems with an uncertain multipath environment. In addition to using a constant velocity motion model, a new terrain-constrained motion model is presented for scenarios where terrain-following is required by motion-constrained autonomous vehicles. The posterior Cramér-Rao lower bound (PCRLB), which quantifies the best possible accuracy in realistic ETT problems with multipath detections and measurement-origin uncertainty, is derived as the benchmark for performance evaluation. Simulations results demonstrate the improved performance of the proposed algorithms.


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    Titel :

    Extended Target Tracking With Multipath Detections, Terrain-Constrained Motion Model and Clutter


    Beteiligte:
    Liu, Ben (Autor:in) / Tharmarasa, Ratnasingham (Autor:in) / Jassemi, Rahim (Autor:in) / Brown, Daly (Autor:in) / Kirubarajan, Thia (Autor:in)


    Erscheinungsdatum :

    01.11.2021


    Format / Umfang :

    3753128 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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