Skip to content
University of Maryland LibrariesDigital Repository at the University of Maryland
    • Login
    View Item 
    •   DRUM
    • A. James Clark School of Engineering
    • Institute for Systems Research Technical Reports
    • View Item
    •   DRUM
    • A. James Clark School of Engineering
    • Institute for Systems Research Technical Reports
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Distributed Detection From Multiple Sensors with Correlated Observations.

    Thumbnail
    View/Open
    TR_89-79.pdf (1.150Mb)
    No. of downloads: 506

    Date
    1989
    Author
    Chau, Yawgeng A.
    Geraniotis, Evaggelos A.
    Metadata
    Show full item record
    Abstract
    We address two problems of memoryless distributed dependent observations across time and sensors. In the first problem, the observation sequence of each sensor consists of common weak signal in additive dependent noise with stationary univariate and second-order joint densities; here the objective of the sensors is to cooperatively detect the presence of a weak signal. In the second problem, the observation sequence of each sensor is characterized by its stationary univariate and second-order pint densities; here the objective of the sensors is to cooperatively discriminate between two arbitrary such sequences of observations. For both problems, the analysis and design are based on a common large sample size. The dependence acms time and sensors is modeled by m-dependent, f-mixing, or p-mixing processes. The performance of the two-sensor configuration for each problem is measured by an average cost, which couples the decisions of the sensors. The design criteria for the test statistics of the sensors, which consist of sums of memoryless nonlinearities, are established by using two-dimensional Chemoff bounds on the associated error probabilities involved in the average cost. The optimal nonlinearities are obtain as the solutions of linear coupled or uncoupled integral equations. Numerical results for specific cases of practical interest show that the performance of the proposed scheme is superior to the one that ignores the dependence across time and/or sensors for each of the two problems.
    URI
    http://hdl.handle.net/1903/4918
    Collections
    • Institute for Systems Research Technical Reports

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility
     

     

    Browse

    All of DRUMCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister
    Pages
    About DRUMAbout Download Statistics

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility