Zhou Baoliang, Zhou Dongming, Gao Hongwei, Yang Jie. Distributed Aperture Coherence-synthetic Radar Joint Antenna Gain Analysis[J]. Journal of Radars, 2017, 6(4): 332-339. doi: 10.12000/JR17055
Citation: WAN Hao and LIANG Jing. HRRP unsupervised target feature extraction method based on multiple contrastive loss in radar sensor networks[J]. Journal of Radars, in press. doi: 10.12000/JR24200

HRRP Unsupervised Target Feature Extraction Method Based on Multiple Contrastive Loss in Radar Sensor Networks

DOI: 10.12000/JR24200
Funds:  The National Natural Science Foundation of China (62471118), and the 111 Project (B17008)
More Information
  • Corresponding author: LIANG Jing, liangjing@uestc.edu.cn
  • Received Date: 2024-10-14
  • Rev Recd Date: 2024-11-08
  • Available Online: 2024-11-11
  • In recent years, target recognition systems based on radar sensor networks have been widely studied in the field of automatic target recognition. These systems observe the target from multiple angles to achieve robust recognition, which also brings the problem of using the correlation and difference information of multiradar sensor echo data. Furthermore, most existing studies used large-scale labeled data to obtain prior knowledge of the target. Considering that a large amount of unlabeled data is not effectively used in target recognition tasks, this paper proposes an HRRP unsupervised target feature extraction method based on Multiple Contrastive Loss (MCL) in radar sensor networks. The proposed method combines instance level loss, Fisher loss, and semantic consistency loss constraints to identify consistent and discriminative feature vectors among the echoes of multiple radar sensors and then use them in subsequent target recognition tasks. Specifically, the original echo data are mapped to the contrast loss space and the semantic label space. In the contrast loss space, the contrastive loss is used to constrain the similarity and aggregation of samples so that the relative and absolute distances between different echoes of the same target obtained by different sensors are reduced while the relative and absolute distances between different target echoes are increased. In the semantic loss space, the extracted discriminant features are used to constrain the semantic labels so that the semantic information and discriminant features are consistent. Experiments on an actual civil aircraft dataset revealed that the target recognition accuracy of the MCL-based method is improved by 0.4% and 1.4%, respectively, compared with the most advanced unsupervised algorithm CC and supervised target recognition algorithm PNN. Further, MCL can effectively improve the target recognition performance of radar sensors when applied in conjunction with the sensors.

     

  • [1]
    陈健, 杜兰, 廖磊瑶. 基于参数化统计模型的雷达HRRP目标识别方法综述[J]. 雷达学报, 2022, 11(6): 1020–1047. doi: 10.12000/JR22127.

    CHEN Jian, DU Lan, and LIAO Leiyao. Survey of radar HRRP target recognition based on parametric statistical model[J]. Journal of Radars, 2022, 11(6): 1020–1047. doi: 10.12000/JR22127.
    [2]
    DONG Ganggang and LIU Hongwei. A hierarchical receptive network oriented to target recognition in SAR images[J]. Pattern Recognition, 2022, 126: 108558. doi: 10.1016/j.patcog.2022.108558.
    [3]
    ZHANG Yukun, GUO Xiansheng, LEUNG H, et al. Cross-task and cross-domain SAR target recognition: A meta-transfer learning approach[J]. Pattern Recognition, 2023, 138: 109402. doi: 10.1016/j.patcog.2023.109402.
    [4]
    CHEN Bo, LIU Hongwei, CHAI Jing, et al. Large margin feature weighting method via linear programming[J]. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(10): 1475–1488. doi: 10.1109/TKDE.2008.238.
    [5]
    MOLCHANOV P, EGIAZARIAN K, ASTOLA J, et al. Classification of aircraft using micro-Doppler bicoherence-based features[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2): 1455–1467. doi: 10.1109/TAES.2014.120266.
    [6]
    LI Xiaoxiong, ZHANG Shuning, ZHU Yuying, et al. Supervised contrastive learning for vehicle classification based on the IR-UWB radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5117312. doi: 10.1109/TGRS.2022.3203468.
    [7]
    CHEN Jian, DU Lan, HE Hua, et al. Convolutional factor analysis model with application to radar automatic target recognition[J]. Pattern Recognition, 2019, 87: 140–156. doi: 10.1016/j.patcog.2018.10.014.
    [8]
    FENG Bo, CHEN Bo, and LIU Hongwei. Radar HRRP target recognition with deep networks[J]. Pattern Recognition, 2017, 61: 379–393. doi: 10.1016/j.patcog.2016.08.012.
    [9]
    DU Lan, LIU Hongwei, WANG Penghui, et al. Noise robust radar HRRP target recognition based on multitask factor analysis with small training data size[J]. IEEE Transactions on Signal Processing, 2012, 60(7): 3546–3559. doi: 10.1109/TSP.2012.2191965.
    [10]
    XU Bin, CHEN Bo, WAN Jinwei, et al. Target-aware recurrent attentional network for radar HRRP target recognition[J]. Signal Processing, 2019, 155: 268–280. doi: 10.1016/j.sigpro.2018.09.041.
    [11]
    SHI Lei, WANG Penghui, LIU Hongwei, et al. Radar HRRP statistical recognition with local factor analysis by automatic Bayesian Ying-Yang harmony learning[J]. IEEE Transactions on Signal Processing, 2011, 59(2): 610–617. doi: 10.1109/TSP.2010.2088391.
    [12]
    LIAO Leiyao, DU Lan, and CHEN Jian. Class factorized complex variational auto-encoder for HRR radar target recognition[J]. Signal Processing, 2021, 182: 107932. doi: 10.1016/j.sigpro.2020.107932.
    [13]
    MAO Chengchen and LIANG Jing. HRRP recognition in radar sensor network[J]. Ad Hoc Networks, 2017, 58: 171–178. doi: 10.1016/j.adhoc.2016.09.001.
    [14]
    LUNDÉN J and KOIVUNEN V. Deep learning for HRRP-based target recognition in multistatic radar systems[C]. 2016 IEEE Radar Conference, Philadelphia, USA, 2016: 1–6. doi: 10.1109/RADAR.2016.7485271.
    [15]
    章鹏飞, 李刚, 霍超颖, 等. 基于双雷达微动特征融合的无人机分类识别[J]. 雷达学报, 2018, 7(5): 557–564. doi: 10.12000/JR18061.

    ZHANG Pengfei, LI Gang, HUO Chaoying, et al. Classification of drones based on micro-Doppler radar signatures using dual radar sensors[J]. Journal of Radars, 2018, 7(5): 557–564. doi: 10.12000/JR18061.
    [16]
    郭帅, 陈婷, 王鹏辉, 等. 基于角度引导Transformer融合网络的多站协同目标识别方法[J]. 雷达学报, 2023, 12(3): 516–528. doi: 10.12000/JR23014.

    GUO Shuai, CHEN Ting, WANG Penghui, et al. Multistation cooperative radar target recognition based on an angle-guided transformer fusion network[J]. Journal of Radars, 2023, 12(3): 516–528. doi: 10.12000/JR23014.
    [17]
    吕小玲, 仇晓兰, 俞文明, 等. 基于无监督域适应的仿真辅助SAR目标分类方法及模型可解释性分析[J]. 雷达学报, 2022, 11(1): 168–182. doi: 10.12000/JR21179.

    LYU Xiaoling, QIU Xiaolan, YU Wenming, et al. Simulation-assisted SAR target classification based on unsupervised domain adaptation and model interpretability analysis[J]. Journal of Radars, 2022, 11(1): 168–182. doi: 10.12000/JR21179.
    [18]
    WU Zhirong, XIONG Yuanjun, YU S X, et al. Unsupervised feature learning via non-parametric instance discrimination[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 3733–3742. doi: 10.1109/CVPR.2018.00393.
    [19]
    ÖZDEMIR C. Inverse Synthetic Aperture Radar Imaging with MATLAB Algorithms[M]. 2nd ed. Hoboken: John Wiley & Sons, 2021: 167–170.
    [20]
    DU Lan, WANG Penghui, LIU Hongwei, et al. Bayesian spatiotemporal multitask learning for radar HRRP target recognition[J]. IEEE Transactions on Signal Processing, 2011, 59(7): 3182–3196. doi: 10.1109/TSP.2011.2141664.
    [21]
    LIAO Kuo, SI Jinxiu, ZHU Fangqi, et al. Radar HRRP target recognition based on concatenated deep neural networks[J]. IEEE Access, 2018, 6: 29211–29218. doi: 10.1109/ACCESS.2018.2842687.
    [22]
    CHEN Ting, KORNBLITH S, NOROUZI M, et al. A simple framework for contrastive learning of visual representations[C]. The 37th International Conference on Machine Learning, Virtual Event, 2020: 149.
    [23]
    WANG Feng and LIU Huaping. Understanding the behaviour of contrastive loss[C]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, 2021: 2495–2504. doi: 10.1109/CVPR46437.2021.00252.
    [24]
    HE Kaiming, FAN Haoqi, WU Yuxin, et al. Momentum contrast for unsupervised visual representation learning[C]. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 9729–9738. doi: 10.1109/CVPR42600.2020.00975.
    [25]
    CARON M, MISRA I, MAIRAL J, et al. Unsupervised learning of visual features by contrasting cluster assignments[C]. The 34th International Conference on Neural Information Processing Systems, Vancouver, Canada, 2020: 831.
    [26]
    LI Yunfan, HU Peng, LIU Zitao, et al. Contrastive clustering[C]. The 35th AAAI Conference on Artificial Intelligence, Virtual Event, 2021: 8547–8555. doi: 10.1609/aaai.v35i10.17037.
    [27]
    PAN Mian, LIU Ailin, YU Yanzhen, et al. Radar HRRP target recognition model based on a stacked CNN–Bi-RNN with attention mechanism[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5100814. doi: 10.1109/TGRS.2021.3055061.
    [28]
    CHEN Jian, DU Lan, GUO Guanbo, et al. Target-attentional CNN for radar automatic target recognition with HRRP[J]. Signal Processing, 2022, 196: 108497. doi: 10.1016/j.sigpro.2022.108497.
    [29]
    WU Lingang, HU Shengliang, XU Jianghu, et al. Ship HRRP target recognition against decoy jamming based on CNN-BiLSTM-SE model[J]. IET Radar, Sonar & Navigation, 2024, 18(2): 361–378. doi: 10.1049/rsn2.12507.
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