Algorithms are presented for managing sensor information to reduce the effects of bias when tracking interacting targets. When targets are close enough together that their measurement validation gates overlap, the measurement from one target can be confused with another. Data association algorithms such as the joint probabilistic data association (JPDA) algorithm can effectively continue to track targets under these conditions, but the target estimates may become biased. A modification of the covariance control approach for sensor management can reduce this effect. Sensors are chosen based on their ability to reduce the extent of measurement gate overlap as judged by a set of heuristic parameters derived in this work. Monte Carlo simulation results show that these are effective methods of reducing target estimate bias in the JPDA algorithm when targets are close together. An analysis of the computational demands of these algorithms shows that while they are computationally demanding, they are not prohibitively so.


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    Title :

    Multisensor covariance control strategies for reducing bias effects in interacting target scenarios


    Contributors:
    Kalandros, M. (author) / Pao, L.Y. (author)


    Publication date :

    2005-01-01


    Size :

    985587 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English