Multistatic active target tracking in GPS-denied scenarios is complicated by the fact that the emitter and receiver locations are unknown and must be estimated jointly with the target track. Maximum a posteriori algorithms for solving this joint estimation problem are complicated by the nonlinearities in the likelihood function of the bistatic range measurement. A new integral representation of this likelihood function is presented for small measurement error variances. Remarkably, target state appears linearly in this integral. This paper presents a new approach to the basic problem of target state estimation for known sensor locations. The optimal estimator derived from the integral representation is an iteratively re-weighted linear Kalman smoother. Joint estimators for target and emitter-receiver field tracking will be reported elsewhere.


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    Title :

    Multistatic Target and Sensor Field Tracking


    Contributors:


    Publication date :

    2007-03-01


    Size :

    431047 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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