Noise-Robust HRRP Target Recognition Based on Residual Scattering Network

被引:0
作者
Huang, Pengjun [1 ]
Li, Shuai [1 ]
Zheng, Muhai [1 ]
Xie, Jingyang [1 ]
Tian, Biao [1 ]
Xu, Shiyou [1 ]
机构
[1] Sun Yat Sen Univ Shenzhen Campus, Sch Elect & Commun Engn, Shenzhen, Peoples R China
来源
2024 9TH INTERNATIONAL CONFERENCE ON SIGNAL AND IMAGE PROCESSING, ICSIP | 2024年
基金
中国国家自然科学基金;
关键词
high-resolution range profile; scattering network; noise-robust feature extraction; radar target recognition;
D O I
10.1109/ICSIP61881.2024.10671484
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Extracting noise-robust features is a key issue for high-resolution range profile (HRRP) target recognition. In order to enhance the recognition performance under low signal-to-noise ratio (SNR), a novel recognition method based on scattering network is proposed in this paper. Specifically, the initial scattering network is enhanced by the incorporation of squared modulus and residual connection, making it more suitable for HRRP. And it is cascaded with ResNet to further extract information and complete the recognition. Experimental results on two different datasets demonstrate the effectiveness and robustness of the proposed method.
引用
收藏
页码:37 / 41
页数:5
相关论文
共 13 条
  • [1] Andreux M, 2020, J MACH LEARN RES, V21
  • [2] Invariant Scattering Convolution Networks
    Bruna, Joan
    Mallat, Stephane
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2013, 35 (08) : 1872 - 1886
  • [3] Target-attentional CNN for Radar Automatic Target Recognition with HRRP
    Chen, Jian
    Du, Lan
    Guo, Guanbo
    Yin, Linwei
    Wei, Di
    [J]. SIGNAL PROCESSING, 2022, 196
  • [4] Tensor RNN With Bayesian Nonparametric Mixture for Radar HRRP Modeling and Target Recognition
    Chen, Wenchao
    Chen, Bo
    Peng, Xiaojun
    Liu, Jiaqi
    Yang, Yang
    Zhang, Hao
    Liu, Hongwei
    [J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2021, 69 : 1995 - 2009
  • [5] Statistical Modeling With Label Constraint for Radar Target Recognition
    Du, Lan
    Chen, Jian
    Hu, Jing
    Li, Yang
    He, Hua
    [J]. IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2020, 56 (02) : 1026 - 1044
  • [6] Radar HRRP target recognition with deep networks
    Feng, Bo
    Chen, Bo
    Liu, Hongwei
    [J]. PATTERN RECOGNITION, 2017, 61 : 379 - 393
  • [7] Multiscale Curvelet Scattering Network
    Gao, Jie
    Jiao, Licheng
    Liu, Fang
    Yang, Shuyuan
    Hou, Biao
    Liu, Xu
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 34 (07) : 3665 - 3679
  • [8] Deep Residual Learning for Image Recognition
    He, Kaiming
    Zhang, Xiangyu
    Ren, Shaoqing
    Sun, Jian
    [J]. 2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, : 770 - 778
  • [9] Radar Target Characterization and Deep Learning in Radar Automatic Target Recognition: A Review
    Jiang, Wen
    Wang, Yanping
    Li, Yang
    Lin, Yun
    Shen, Wenjie
    [J]. REMOTE SENSING, 2023, 15 (15)
  • [10] Deep Scattering Power Spectrum Features for Robust Speech Recognition
    Joy, Neethu M.
    Oglic, Dino
    Cvetkovic, Zoran
    Bell, Peter
    Renals, Steve
    [J]. INTERSPEECH 2020, 2020, : 1673 - 1677