The optimal detector of the moderately fluctuating model has been shown to be a weighted noncoherent integrator of sampled pulse returns. Nevertheless, the calculation of the optimal weights in the optimal detector needs dynamic inversion of a covariance matrix, which results in huge computation complexity. In this paper, a general description of the hybrid integration detector (HID) for moderately fluctuating target is given to achieve a good balance between detection performance and computation complexity. In the HID, the integration time is divided into several subapertures with coherent integration implemented within each subaperture, with noncoherent integration among subapertures. To obtain the optimal detection performance of the HID, the optimal length of subaperture is derived and theoretical detection threshold is given, based on which an optimal HID (OHID) is proposed for the detection of moderately fluctuating Rayleigh targets in noise background. Furthermore, according to the fluctuation degree which can be reflected by the relationship of detection performance between two special cases of HID, i.e., coherent integration detector and noncoherent integration detector, three kinds of target types are defined, namely, strong, ordinary, and weak fluctuating targets. At the cost of small detection performance loss of these moderately fluctuating targets, the proposed OHID can improve the computation efficiency remarkably. Numerical experiments are provided to demonstrate the effectiveness and efficiency of the proposed method.


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    Title :

    Radar Detection of Moderately Fluctuating Target Based on Optimal Hybrid Integration Detector


    Contributors:
    Zhou, Xu (author) / Qian, Lichang (author) / Ding, Zegang (author) / Xu, Jia (author) / Liu, Weijian (author) / You, Pengjie (author) / Long, Teng (author)


    Publication date :

    2019-10-01


    Size :

    3759255 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English