The well-known cell-averaging constant false alarm rate (CA-CFAR) scheme and its variants suffer from masking effect in multi-target scenarios. Although order-statistic CFAR (OS-CFAR) scheme performs well in such scenarios, it is compromised with high computational complexity. To handle masking effects with a lower computational cost, in this paper, we propose a deep-learning based CFAR (DL- CFAR) scheme. DL-CFAR is the first attempt to improve the noise estimation process in CFAR based on deep learning. Simulation results demonstrate that DL-CFAR outperforms conventional CFAR schemes in the presence of masking effects. Furthermore, it can outperform conventional CFAR schemes significantly under various signal-to-noise ratio conditions. We hope that this work will encourage other researchers to introduce advanced machine learning technique into the field of target detection.


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    Title :

    DL-CFAR: A Novel CFAR Target Detection Method Based on Deep Learning


    Contributors:
    Lin, Chia-Hung (author) / Lin, Yu-Chien (author) / Bai, Yue (author) / Chung, Wei-Ho (author) / Lee, Ta-Sung (author) / Huttunen, Heikki (author)


    Publication date :

    2019-09-01


    Size :

    1994607 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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