Extended target detection in the presence of K-distributed clutter has gained a special interest in recent years. High-resolution radars allow a target to be found in several resolution cells. Therefore, the detection rate and the false alarm rate of an extended target should be analyzed by these cells jointly rather than in one single resolution cell. A detector in which all the cells whose magnitude affected by one target are considered jointly is present. The performance and optimal parameters of the detector are analyzed in detail. Meanwhile, the large amount of calculation caused by enormous raw data is also considered. Then, an efficient method based on region growing algorithm and contour tracking algorithm is proposed. Only part of the resolution cells is scanned once with the proposed method, while all the cells are scanned at least one times in the existing methods. Therefore, considerable calculations are saved. Furthermore, the proposed method and several existing methods are performed with real data and simulated data, and the results show that the proposed model is practical and efficient to detect the extended targets in K-distributed clutters.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    An efficient extended target detection method based on region growing and contour tracking algorithm


    Contributors:
    Yan, Bo (author) / Xu, LP (author) / Yan, JZH (author) / Li, Cong (author)


    Publication date :

    2018-04-01


    Size :

    12 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Efficient Multi-Sensor Extended Target Tracking using GM-PHD Filter

    Ahrabian, Alireza / Emambakhsh, Mehryar / Sheeny, Marcel et al. | IEEE | 2019



    Multistatic Bayesian extended target tracking

    Vivone, Gemine / Braca, Paolo / Granstrom, Karl et al. | IEEE | 2016


    Eikonal-based region growing for efficient clustering

    Buyssens, P. / Gardin, I. / Ruan, S. et al. | British Library Online Contents | 2014