Abstract This paper is concerned with the distributed Kalman state estimation with an adaptive consensus factor for a discrete-time target linear system over a sensor network. Both optimal filter gain and average disagreement of the estimates are considered in the filter design. In order to estimate the state of the target more accurately, an optimal Kalman gain is obtained by minimizing the mean-squared estimation error. The considered disagreement is employed to adjust the optimal gain as well as to acquire a better filtering performance. An illustrative example has been presented to prove the correctness of the conclusion and show the tracking performance of the filters.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Adaptive Consensus-Based Distributed Target Tracking in Sensor Networks


    Contributors:
    Zhou, Xue (author) / Zhang, Hao (author) / Yan, Huaicheng (author)


    Publication date :

    2017-01-01


    Size :

    8 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English