Event region detection refers to the process of detecting regions with distinguishable characteristics in an environment, and it can find a broad range of applications from environmental monitoring to system health management. The problem of online dynamic event region detection is studied here. The spatiotemporal relationship of the evolving event regions is assumed and modeled by dynamic Markov random felds. Observations are collected from a network of sensors distributed in the feld. To provide detection results at each time step, a distributed event region tracking algorithm is proposed. The system dynamics and information collected from neighbors are used to predict the underlying hypothesis at each sensor node and its local observation is used for update. Mean feld approximation is adopted in the algorithm for tractability. The performance of the proposed algorithm is analyzed both theoretically and through simulations. By comparing with static event region detection algorithms and a centralized algorithm (with certain approximation), we demonstrate the effectiveness and efficiency of the proposed algorithm, especially its robustness in low signal-to-noise ratio (SNR) situations.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Highlighted AESS Transactions article


    Contributors:
    Wu, Tao (author) / Cheng, Qi (author)


    Publication date :

    2014-05-01


    Size :

    446183 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Inside AESS - AESS Fellows

    Online Contents | 1993



    AESS Tutorials

    Online Contents | 2011


    AESS Conferences

    LeFevre | Online Contents | 1997