Journal of Systems Engineering and Electronics ›› 2024, Vol. 35 ›› Issue (6): 1428-1440.doi: 10.23919/JSEE.2024.000096

• DEFENCE ELECTRONICS TECHNOLOGY • Previous Articles    

Sea clutter suppression via cuttable encoder-decoder-augmentation network

Chuanfei ZANG1(), Yumiao WANG1(), Xiang WANG1(), Congan XU2,3(), Guolong CUI1,*()   

  1. 1 School of Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
    2 Advanced Technology Research Institute, Beijing Institute of Technology, Jinan 250300, China
    3 Naval Aviation University, Yantai 264000, China
  • Received:2023-01-03 Online:2024-12-18 Published:2025-01-14
  • Contact: Guolong CUI E-mail:zangcf2017@163.com;wangyumiao@std.uestc.edu.cn;w_xiang@std.uestc.edu.cn;xcatougao@163.com;cuiguolong@uestc.edu.cn
  • About author:
    ZANG Chuanfei was born in 1999. He received his B.S. degree in Jilin University, Changchun, China, in 2021. He is currently pursuing his Ph.D. degree in University of Electronic Science and Technology of China, Chengdu, China. His research interests include target detection and deep learning. E-mail: zangcf2017@163.com

    WANG Yumiao was born in 1996. She received her B.S. degree in Nanchang Hangkong University, Nanchang, China, in 2018. She is currently pursuing her Ph.D. degree in University of Electronic Science and Technology of China, Chengdu, China. Her research interests include target detection, sea clutter suppression, and deep learning. E-mail: wangyumiao@std.uestc.edu.cn

    WANG Xiang was born in 1997. He received his B.S. degree in Anhui University, Hefei, China, in 2019. He is currently pursuing his Ph.D. degree in University of Electronic Science and Technology of China, Chengdu, China. His research interests include radar signal processing and machine learning. E-mail: w_xiang@std.uestc.edu.cn

    XU Congan was born in 1987. He received his M.S. and Ph.D. degrees from Naval Aviation University, Yantai, China, in 2013 and 2016, respectively. He is currently a lecturer in Naval Aviation University. His research interests include intelligent perception and fusion, deep learning and its application. E-mail: xcatougao@163.com

    CUI Guolong was born in 1982. He received his B.S. degree in electronic information engineering, M.S. and Ph.D. degrees in signal and information processing from the University of Electronic Science and Technology of China, Chengdu, China, in 2005, 2008, and 2012, respectively. He is currently a professor in University of Electronic Science and Technology of China. His research interests include cognitive radar, array signal processing, and through-the-wall radar. E-mail: cuiguolong@uestc.edu.cn
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (62271126).

Abstract:

This paper considers the problem of sea clutter suppression. We propose the cuttable encoder-decoder-augmentation network (CEDAN) to improve clutter suppression performance by enriching the contrast information between the target and clutter. Specifically, the plug-and-play residual U-block (ResUblock) is proposed to augment the feature representation ability of the clutter suppression model. The CEDAN first extracts and fuses the multi-scale features using the encoder and the decoder composed of the ResUblocks. Then, the fused features are processed by the contrast information augmentation module (CIAM) to enhance the diversity of target and clutter, resulting in encouraging sea clutter suppression results. In addition, we propose the result-consistency loss to further improve the suppression performance. The result-consistency loss enables CEDAN to cut some blocks of decoder and CIAM to reduce the inference time without significantly degrading the suppression performance. Experimental results on measured and simulated data show that the CEDAN outperforms state-of-the-art sea clutter suppression methods in sea clutter suppression performance and computation efficiency.

Key words: radar, sea clutter suppression, deep learning