A new class of techniques for multisensor fusion and target recognition is proposed using sequence comparison by dynamic programming and multiple model estimation. The objective is to fuse information on the kinematic state and "nonkinematic" signature of unclassified targets, assessing the joint likelihood of all observed events for recognition. Relationships are shown to previous efforts in pattern recognition and state estimation. This research applies "classical" speech processing-related and other sequence comparison methods to moving target recognition, extends the efforts of previous researchers through improved fusion with kinematic information, relates the proposed techniques to Bayesian theory, and applies parameter identification methods to target recognition for improved understanding of the subject in general. The proposed techniques are evaluated and compared with existing approaches using the method of generalized ambiguity functions, which lends to a form of Cramer-Rao lower bound for target recognition.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Sequence comparison techniques for multisensor data fusion and target recognition


    Contributors:
    Libby, E.W. (author) / Maybeck, P.S. (author)


    Publication date :

    1996-01-01


    Size :

    2104814 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Applications of Multisensor Data Fusion to Target Recognition

    Moruzzis, M. / Colin, N. / Milhem, G. et al. | British Library Conference Proceedings | 1995


    Multisensor data fusion

    Varshney, P.K. | Tema Archive | 1997


    Multisensor data fusion

    Waltz, E. / Llinas, J. | Tema Archive | 1990


    Distributed multisensor data fusion

    NASBURG, R. / MORAVEC, K. | AIAA | 1984