Yang Wei, Zhou Qiang, Yuan Mingchen, Li Yang, Wang Yanhua, Zhang Liang
Radar Research Laboratory, School of Information and Electronics, Beijing Institute of Technology, Beijing, China.
Electromagnetic Sensing Research Center of CEMEE State Key Laboratory, School of Information and Electronics, Beijing Institute of Technology, Beijing, China.
Front Neurosci. 2023 Aug 22;17:1252179. doi: 10.3389/fnins.2023.1252179. eCollection 2023.
Radar high-resolution range profile (HRRP) provides geometric and structural information of target, which is important for radar automatic target recognition (RATR). However, due to the limited information dimension of HRRP, achieving accurate target recognition is challenging in applications. In recent years, with the rapid development of radar components and signal processing technology, the acquisition and use of target multi-frequency and polarization scattering information has become a significant way to improve target recognition performance. Meanwhile, deep learning inspired by the human brain has shown great promise in pattern recognition applications. In this paper, a Multi-channel Fusion Feature Extraction Network (MFFE-Net) inspired by the human brain is proposed for dual-band polarimetric HRRP, aiming at addressing the challenges faced in HRRP target recognition. In the proposed network, inspired by the human brain's multi-dimensional information interaction, the similarity and difference features of dual-frequency HRRP are first extracted to realize the interactive fusion of frequency features. Then, inspired by the human brain's selective attention mechanism, the interactive weights are obtained for multi-polarization features and multi-scale representation, enabling feature aggregation and multi-scale fusion. Finally, inspired by the human brain's hierarchical learning mechanism, the layer-by-layer feature extraction and fusion with residual connections are designed to enhance the separability of features. Experiments on simulated and measured datasets verify the accurate recognition capability of MFFE-Net, and ablative studies are conducted to confirm the effectiveness of components of network for recognition.
雷达高分辨率距离像（HRRP）提供了目标的几何和结构信息，这对于雷达自动目标识别（RATR）至关重要。然而，由于HRRP的信息维度有限，在应用中实现准确的目标识别具有挑战性。近年来，随着雷达组件和信号处理技术的快速发展，目标多频和极化散射信息的获取与利用已成为提高目标识别性能的重要途径。同时，受人类大脑启发的深度学习在模式识别应用中显示出巨大潜力。本文针对双频极化HRRP提出了一种受人类大脑启发的多通道融合特征提取网络（MFFE-Net），旨在解决HRRP目标识别中面临的挑战。在所提出的网络中，受人类大脑多维信息交互的启发，首先提取双频HRRP的相似性和差异性特征，以实现频率特征的交互融合。然后，受人类大脑选择性注意机制的启发，获得多极化特征和多尺度表示的交互权重，实现特征聚合和多尺度融合。最后，受人类大脑分层学习机制的启发，设计了带有残差连接的逐层特征提取和融合，以增强特征的可分离性。在模拟和实测数据集上的实验验证了MFFE-Net的准确识别能力，并进行了消融研究以确认网络组件对识别的有效性。
