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

DOI: 10.12000/JR24200

  • School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds:  The National Natural Science Foundation of China (62471118), and the 111 Project (B17008)
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  • Corresponding author: LIANG Jing, liangjing@uestc.edu.cn
  • Received Date: 2024-10-14
  • Rev Recd Date: 2024-11-08
    • Available Online: 2024-11-11
    Abstract
  • 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.

     

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