Data association is a fundamental problem in multitarget-multisensor tracking. It entails selecting the most probable association between sensor measurements and target tracks from a very large set of possibilities. A solution is infeasible by direct computation even in modestly-sized applications. We describe an iterative method for solving the optimal data association problem in a distributed fashion; the work exploits the framework of graphical models, a powerful tool for encoding the statistical dependencies of a set of random variables. Our basic idea is to treat the measurement assignment for each sensor as a random variable, which is in turn represented as a node in an underlying graph. Neighboring nodes are coupled by the targets visible to both sensors. Thus we transform the data association problem to that of computing the maximum a posteriori (MAP) configuration in a graphical model to which efficient techniques can be applied. We use a tree-reweighted version of the usual max-product algorithm that either outputs the MAP data association, or acknowledges failure. For acyclic graphs, this message-passing algorithm can solve the data association problem directly and recursively with complexity O((n )2N) for N sensors and n targets. On graphs with cycles, the algorithm may require more iterations to converge, and need not output an unambiguous assignment. However, for the data association problems we consider, the coupling matrices involved in computations are inherently of low rank, and experiments show that the algorithm converges very quickly and finds the MAP configurations.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multitarget-multisensor data association using the tree-reweighted max-product algorithm


    Contributors:
    Chen Lei (author) / Wainwright, M.J. (author) / Cetin, M. (author) / Willsky, A.S. (author)


    Publication date :

    2003


    Size :

    12 Seiten, 19 Quellen




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English





    A multisensor-multitarget data association algorithm for heterogeneous sensors

    Deb, S. / Pattipati, K.R. / Bar-Shalom, Y. | IEEE | 1993


    Data association in multitarget tracking with multisensor

    Song Xiaoquan / Mo Longbin / Lin Qi et al. | IEEE | 1997


    Data Association in Multitarget Tracking with Multisensor

    Song, X. / Sun, Z. / Mo, L. et al. | British Library Conference Proceedings | 1997


    Multisensor-Multitarget Tracking

    Online Contents | 1996