This paper presents a Bayesian algorithm for joint detection and tracking in a multitarget setting. Raw measurements are processed using the track-before-detect (TBD) framework. We first establish a Bayesian recursion, which propagates a probability of target existence along with a target state probability density per delay/Doppler bin. In order to handle the nonlinearity of the observation model obtained for orthogonal frequency division multiplexing (OFDM)-based passive radar, a suitable Gaussian mixture implementation is proposed.


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    Title :

    Recursive Bayesian Filtering for Multitarget Track-Before-Detect in Passive Radars


    Contributors:
    Lehmann, F. (author)


    Publication date :

    2012-07-01


    Size :

    3363765 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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