雷达微弱目标智能化处理技术与应用

Radar Intelligent Processing Technology and Application for Weak Target

雷达微弱目标处理是实现优异探测性能的基础和前提,在复杂的实际环境应用过程中,由于强杂波干扰、目标信号微弱、图像特征不明显、有效特征难提取等问题,导致雷达微弱目标检测与识别一直是雷达处理领域中的难点之一。传统模型类处理方法与实际工作背景和目标特性匹配不精准,导致通用性不强。近年来,深度学习在雷达智能信息处理领域取得了显著进展,深度学习算法通过构建深层神经网络,可以自动地从大量雷达数据中学习特征表示,提高目标检测和识别的性能。该文分别从雷达目标微弱信号处理、图像处理、特征学习等多个方面系统梳理和总结近年来雷达微弱目标智能化处理的研究进展,具体包括噪声与杂波抑制、微弱目标信号增强;低、高分辨雷达图像和特征图处理;特征提取、融合、目标分类与识别等。针对目前微弱目标智能化处理应用存在的泛化能力有限、特征单一、可解释性不足等问题,从小样本目标检测(迁移学习、强化学习)、多维多特征融合检测、网络模型可解释性、知识与数据联合驱动等方面对未来发展进行了展望。

Weak target signal processing is the cornerstone and prerequisite for radar to achieve excellent detection performance.In complex practical applications,due to strong clutter interference,weak target signals,unclear image features,and difficult effective feature extraction,weak target detection and recognition have always been challenging in the field of radar processing.Conventional model-based processing methods do not accurately match the actual working background and target characteristics,leading to weak universality.Recently,deep learning has made significant progress in the field of radar intelligent information processing.By building deep neural networks,deep learning algorithms can automatically learn feature representations from a large amount of radar data,improving the performance of target detection and recognition.This article systematically reviews and summarizes recent research progress in the intelligent processing of weak radar targets in terms of signal processing,image processing,feature extraction,target classification,and target recognition.This article discusses noise and clutter suppression,target signal enhancement,low-and high-resolution radar image and feature processing,feature extraction,and fusion.In response to the limited generalization ability,single feature expression,and insufficient interpretability of existing intelligent processing applications for weak targets,this article underscores future developments from the aspects of small sample object detection(based on transfer learning and reinforcement learning),multidimensional and multifeature fusion,network model interpretability,and joint knowledge-and data-driven processing.