基于时频分析的SAR目标微波视觉特性智能感知方法与应用  

Physically Explainable Intelligent Perception and Application of SAR Target Characteristics Based on Time-frequency Analysis

作　　者：黄钟泠 吴冲 姚西文[1] 王立鹏 韩军伟[1] HUANG Zhongling;WU Chong;YAO Xiwen;WANG Lipeng;HAN Junwei(School of Automation,Northwestern Polytechnical University,Xi’an 710072,China;Institute of Mechanical and Electrical Engineering,Beijing 100074,China)

机构地区：[1]西北工业大学自动化学院,西安710072 [2]北京机电工程研究所,北京100074

出　　处：《雷达学报（中英文）》2024年第2期331-344,共14页Journal of Radars

基　　金：国家自然科学基金(62101459);中国博士后科学基金(BX2021248)。

摘　　要：合成孔径雷达(SAR)目标识别智能算法目前仍面临缺少鲁棒性、泛化性和可解释性的挑战,理解SAR目标微波特性并将其结合先进的深度学习算法,实现高效鲁棒的SAR目标识别,是目前领域较为关注的研究重点。SAR目标特性反演方法通常计算复杂度较高,难以结合深度神经网络实现端到端的实时预测。为促进SAR目标物理特性在智能识别任务中的应用,发展高效、智能、可解释的微波物理特性感知方法至关重要。该文将高分辨SAR目标的非平稳特性作为一种典型的微波视觉特性,提出一种改进的基于时频分析的目标特性智能感知方法,优化了处理流程和计算效率,使之更适用于SAR目标识别场景,并进一步将其应用到SAR目标智能识别算法中,实现了稳定的性能提升。该方法泛化性强、计算效率高,能得到物理可解释的SAR目标特性分类结果,对目标识别算法的性能提升与属性散射中心模型相当。The current state of intelligent target recognition approaches for Synthetic Aperture Radar(SAR)continues to experience challenges owing to their limited robustness,generalizability,and interpretability.Currently,research focuses on comprehending the microwave properties of SAR targets and integrating them with advanced deep learning algorithms to achieve effective and resilient SAR target recognition.The computational complexity of SAR target characteristic-inversion approaches is often considerable,rendering their integration with deep neural networks for achieving real-time predictions in an end-to-end manner challenging.To facilitate the utilization of the physical properties of SAR targets in intelligent recognition tasks,advancing the development of microwave physical property sensing technologies that are efficient,intelligent,and interpretable is imperative.This paper focuses on the nonstationary nature of high-resolution SAR targets and proposes an improved intelligent approach for analyzing target characteristics using time-frequency analysis.This method enhances the processing flow and calculation efficiency,making it more suitable for SAR targets.It is integrated with a deep neural network for SAR target recognition to achieve consistent performance improvement.The proposed approach exhibits robust generalization capabilities and notable computing efficiency,enabling the acquisition of classification outcomes of the SAR target characteristics that are readily interpretable from a physical standpoint.The enhancement in the performance of the target recognition algorithm is comparable to that achieved by the attribute scattering center model.

关 键 词：合成孔径雷达(SAR) 目标识别 目标特性 微波视觉 时频分析(TFA) 

分 类 号：TN957.51[电子电信—信号与信息处理]