Target tracking in a widely spread multiple input multiple output (MIMO) radar system requires joint processing of several measurements from multiple sensors. The probability hypothesis density (PHD) filter provides a promising framework to process these measurements, since it does not require any measurement-to-track associations. Furthermore, the PHD filter naturally handles a multi-target environment because of the lack of explicit data association. We implement a PHD filter in the GTRI/ONR MIMO Benchmark, and compare results against the Benchmark's default solution. We assume a linear Gaussian target model so that the posterior target intensity at any time step is a Gaussian mixture (GM). Under this assumption, the PHD filter has closed-form recursions and target state extraction is simplified. This paper focuses on our implementationofthe GM-PHDfilterinthe MIMO Benchmark, along with practical issues such as track labeling and applying the filter for the case of multiple sensors.


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    Title :

    MIMO radar target tracking using the probability hypothesis density filter


    Contributors:


    Publication date :

    2012-03-01


    Size :

    597207 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English