摘要
非视距(NLOS)毫米波雷达三维成像利用电磁波反射、衍射、散射、穿透等传播特性,实现对隐蔽环境目标的探测、定位和成像,在无人驾驶、灾害救援、城市作战等领域具有重要应用潜力。然而,受实际非视距场景中反射面、遮挡面等不确定性引入的相位误差、孔径遮蔽、多径效应影响,雷达成像出现分辨率差、伪影增多等问题。针对上述问题,结合深度展开网络和环境先验感知,该文提出了一种基于距离徙动(RM)算子学习驱动的非视距毫米波雷达三维成像方法。首先,建立了拐角(LAC)场景下非视距毫米波雷达三维成像模型,引入RM核算子提高成像效率,降低计算复杂度;其次,构建了一种基于快速迭代收缩阈值(FISTA)框架的高精度非视距三维成像网络,利用非视距场景特性,将算法参数设计为网络权重的函数,实现非视距目标高精度、高效率三维重构;最后,搭建了近场非视距毫米波雷达成像平台,完成了理想与非理想反射面场景下金属字母“O”“S”以及埃菲尔铁塔模型、人造卫星模型等目标的实验验证,结果表明所提方法在提升三维成像精度的同时,运行速度较传统稀疏成像算法提升了两个数量级。
Non-Line-Of-Sight(NLOS)millimeter wave radar 3D imaging leverages electromagnetic wave propagation characteristics such as reflection,diffraction,scattering,and penetration to detect,locate,and image hidden targets in occluded environments.It holds significant potential for applications in autonomous driving,disaster rescue,and urban warfare.However,uncertainties introduced by reflection surfaces and occluding objects in practical NLOS scenarios,such as phase errors,aperture shadowing,and multipath effect,lead to issues like blurred imaging and increased artifacts in radar imaging.To address these challenges,this study proposes a 3D imaging method for NLOS millimeter wave radar based on Range Migration(RM)operator learning,leveraging the adaptive optimization properties of deep unfolding networks and prior environmental perception.First,a 3D imaging model for NLOS millimeter wave radar in Looking Around Corner(LAC)scenarios is established.An RM kernel operator is introduced to enhance imaging efficiency and reduce computational complexity.Second,a high-precision NLOS 3D imaging network is constructed based on the Fast Iterative Shrinkage/Thresholding Algorithm(FISTA)framework.Utilizing features specific to NLOS scenes and designing algorithm parameters as functions of network weights,the method achieves high-precision,high-efficiency 3D reconstruction of NLOS targets.Finally,a near-field NLOS millimeter wave radar imaging platform is developed.Experimental validations are performed on targets,including metal letters“O”and“S”,an Eiffel Tower model,and an artificial satellite model,under both ideal and non-ideal reflection surface conditions.The results demonstrate that the proposed method significantly improves 3D imaging precision,achieving a two-orders-of-magnitude increase in computational speed over traditional sparse imaging algorithms.
作者
陈锟
韦顺军
蔡响
王谋
张浩
崔国龙
张晓玲
陈思远
CHEN Kun;WEI Shunjun;CAI Xiang;WANG Mou;ZHANG Hao;CUI Guolong;ZHANG Xiaoling;CHEN Siyuan(School of Information and Communication Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China;Sichuan Provincial Key Laboratory of Precision Measurement Radar System Technology,Chengdu 611731,China;Beijing Institute of Control and Electronics Technology,Beijing 100038,China)
出处
《雷达学报(中英文)》
北大核心
2026年第1期42-63,共22页
Journal of Radars
基金
国家自然科学基金(62271108,62401119)
四川省自然科学基金(2025ZNSFSC0526)。
关键词
非视距成像
毫米波雷达
多径散射
深度展开
稀疏重构
Non-Line-Of-Sight(NLOS)imaging
Millimeter wave radar
Multipath scattering
Deep unfolding
Sparse reconstruction
