雷达学报杂志

雷达学报杂志2023年第1期

• 天线阵列编码合成孔径成像

  王岩飞, 李和平, 韩松

  合成孔径雷达(SAR)方位理论分辨率为天线长度的二分之一，使得SAR高分辨率、远距离成像对天线的要求相互矛盾。该文提出了对天线阵列编码的合成孔径成像方法，通过将长天线分解为子阵、发射不同的信号进行阵列编码、协同工作，提高空间能量利用率，实现子阵小天线的高分辨率以及全阵列长天线的高增益，从而解决了高分辨率与远距离成像难以同时兼顾的问题。在介绍阵列编码基本概念的基础上，给出了阵列编码雷达成像模型及处理流程，对系统的分辨率、信噪比、脉冲重复频率(PRF)及距离方位模糊等性能进行了理论分析与探讨。在飞行测试实验中，用4个子阵获取了方位分辨率优于0.1 m、幅宽超过8 km的连续条带图像，打破了传统SAR采用聚束模式实现高分辨率时只能小范围成像的制约，新方法为解决传统SAR的原理限制问题提供了有效的途径。同时，通过阵列编码扩展了信号维度，为雷达系统能力的增强提供了技术基础。理论分析及实验结果验证了该文天线阵列编码方法的显著优势及工程实现的可行性。

• 星源照射双/多基地SAR成像

  武俊杰, 孙稚超, 吕争, 杨建宇, 李财品, 孙华瑞, 陈天夫, 赵良波, 任航, 庄超然

  星源照射双/多基地合成孔径雷达(SAR)，采用卫星发射，卫星、临近空间、飞机、地面等平台接收，实现对地海面场景和目标的高分辨成像。该技术具有可成像范围广、隐蔽性好、抗干扰能力强等优点，且可以通过波束调控实现扫描、聚束、滑动聚束等多种组合成像模式，从而获取更加丰富的成像信息，具有十分广阔的民用和军事应用前景。目前，国内外针对星源照射双/多基地SAR成像技术开展了多年的研究，积累了诸多研究成果。该文分别从系统组成、构型方法、回波模型、成像方法、收发同步与试验验证等方面对该技术进行阐述与分析，同时对相关的研究工作进行较系统的回顾，并展望了星源照射双/多基地SAR成像技术未来的发展方向。

• 基于自学习稀疏先验的三维SAR成像方法

  王谋, 韦顺军, 沈蓉, 周梓晨, 师君, 张晓玲

  合成孔径雷达三维成像技术(3D SAR)能通过孔径维度扩展实现三维成像能力，但数据维度大、系统实现难、成像分辨率低。压缩感知稀疏重构技术在简化3D SAR系统、提升成像质量等方面展现出巨大潜力，但面临计算复杂度高、参数设置困难、弱稀疏场景适应差等新问题，制约了其实际应用。针对上述问题，该文结合卷积神经网络的特征学习及迭代算法的深度展开理论，提出了基于自学习稀疏先验的3D SAR成像方法。首先，探讨了常规3D SAR稀疏成像中矩阵向量线性表征模型的局限性，引入成像算子提升成像算法处理效率。其次，讨论了迭代算法映射网络的深度展开模型和实现方式，包括网络拓扑结构设计、算法参数的优化约束及网络的训练方法。最后，通过仿真数据和地面实验，证明了所提方法在提升成像精度的同时，其运行时间较传统稀疏成像算法降低一个数量级。

• 3D SAR Imaging Method Based on Learned Sparse Prior（in English）

  The development of 3D Synthetic Aperture Radar (SAR) imaging is currently hampered by issues such as high data dimension, high system complexity, and low imaging processing efficiency. Sparse SAR imaging has grown in importance as a research branch in SAR imaging due to the high potential of sparse signal processing techniques based on Compressed Sensing (CS) to show high potential in reducing system complexity and improving imaging quality. However, traditional sparse imaging methods are still constrained by high computational complexity, nontrivial parameter tuning, and poor adaptability to weakly sparse scenes. To address these issues, we propose a new 3D SAR imaging method based on learned sparse priors inspired by the deep unfolding concept. First, the limitations of the matrix-vector linear representation model are discussed, and an imaging operator is introduced to improve the algorithm’s imaging efficiency. Furthermore, this research focuses on algorithm network details, such as network topology design, the problem of complex-valued propagations, optimization constraints of algorithm parameters, and network training details. Finally, through simulations and measured experiments, it is proved that the proposed method can improve the imaging accuracy while reducing the running time by more than one order of magnitude compared with the conventional sparse imaging algorithms.

• 地基大视场SAR形变监测的非均匀大气相位校正方法

  白泽朝, 王彦平, 王振海, 胡俊, 李洋, 林赟

  大气变化是地基干涉合成孔径雷达(GB-InSAR)形变监测的主要干扰因素。由于监测环境的地形复杂，水汽、湿度和温度的空间异质性，基于均匀大气介质假设的校正方法可能导致大气校正精度较低。该文提出了一种两阶段半经验模型，用于估计和校正复杂大气条件下特大滑坡GB-InSAR监测过程中出现的大气相位误差。该方法兼顾线性大气相位和非线性大气相位，首先根据测区的范围和高程对观测到的大气相位进行建模，校正与地形相关的线性大气相位。然后，考虑复杂大气条件和方位向大视场角度场景下出现的空间域非均匀大气情况，选取稳定永久散射体(PS)通过插值的方式获取所有PS点的大气相位，校正非线性大气相位。采用该方法对三峡库区新铺和藕塘特大滑坡的地基大视场雷达图像进行处理，相比于常规方法减小大气相位误差最大约2 mm。能有效校正特大滑坡监测场景下出现的非均匀大气相位，满足特大滑坡广域范围监测需求。

• 多策略ATLAS数据优选与影像匹配相结合的SAR高程控制点提取

  余洋, 靳国旺, 熊新, 童成功, 张国平

  为了精化星载SAR影像几何参数并提高立体定位精度，借鉴星载激光测高数据光学遥感影像高程控制点提取思路，设计了一种多策略高级地形激光测高系统(ATLAS)数据优选与影像匹配相结合的SAR高程控制点提取方法。该方法采用非夜间观测光子滤除、高置信度光子选取、SRTM DEM辅助的粗差剔除、大偏心率椭圆滤波核平坦区域光子筛选等多种策略，从ATLAS数据ATL03级产品中提取高质量、平坦区域的激光高程点，再依据SRTM DEM对斜距SAR影像进行地理编码，按激光高程点的平面坐标选取局部谷歌地球影像作为足印影像，采用秩自相似描述子进行足印影像与SAR地理编码影像的匹配，得到与激光高程点对应的SAR影像像点坐标，从而提取SAR高程控制点。采用中国登封市、日本横须贺市两个区域的ATLAS数据进行了高分三号SAR高程控制点提取实验，利用提取的高程控制点进行SAR影像几何参数精化，大幅提升了立体定位精度，验证了该文高程控制点提取方法的可行性和有效性。

• 复杂动态海面与目标电磁散射及回波仿真研究现状与展望

  郭立新, 魏仪文

  海洋表面是一种高度不规则和时空不重复的复杂动态体系。海杂波是雷达电磁信号照射到海面产生的大量散射体回波的叠加，受风力、洋流、海浪等的影响呈现非均匀性和非平稳性。海杂波信号对海上目标的探测具有一定的干扰作用，尤其是高海情条件下，海浪起伏更加剧烈，目标信号极易淹没在强海杂波信号中，严重限制着雷达对海上目标的检测能力。海杂波及目标电磁散射特性研究是提升复杂海洋环境下目标检测能力的基础，以电磁波与实际复杂动态海面及目标电磁散射机理为基础，形成实际海洋环境下目标回波数据，对海杂波及目标雷达回波特征分析，实测数据集的补充，均存在重大意义。为了让更多相关研究者获得基于物理机理的复杂海环境与目标回波仿真方法近些年的发展和未来趋势，该文总结了回波仿真的3类方法，并针对海面与目标仿真场景特点，分析了3类方法的优劣和适应性，给出了部分仿真结果；还介绍了一些基于实测的回波数据集，可方便学者对回波特性进行分析；最后对复杂海面与目标回波仿真方法和特性研究的发展趋势进行了展望。

• Status and Prospects of Electromagnetic Scattering Echoes Simulation from Complex Dynamic Sea Surfaces and Targets（in English）

  The ocean surface is a complicated dynamic system with considerable irregularity and nonrepetition in space and time. Sea clutter is the superposition of a large number of scatterer echoes generated by the radar electromagnetic signal irradiated to the sea surface, which is affected by wind, currents, waves, etc. and shows nonuniformity and nonsmoothness. The sea clutter signal has a certain interference effect on the detection of sea targets, especially under high sea conditions when the waves are furious, and the target signal is readily drowned out by the strong sea clutter signal, severely limiting the radar’s detection capability on sea targets. The investigation of sea clutter and target electromagnetic scattering properties serves as the foundation for improving the target detection capability in difficult marine environments. The formation of target echo data in the actual marine environment is of great significance for the analysis of sea clutter and target radar echo characteristics, as well as the supplementation of the actual measurement data set based on electromagnetic waves and the actual complex dynamic sea surface and target electromagnetic scattering mechanism. This study summarizes three key categories of echo simulation methods, analyzes the benefits, disadvantages, and adaptability of several categories of methods for the characteristics of the sea surface and target simulation scenarios, and provides some simulation results in order to make recent advancements and future trends of physics-based complex sea environment and target echo simulation methods more accessible to relevant researchers. It also introduces some echo datasets based on real measurements, which can facilitate scholars’ analysis of echo characteristics. Lastly, the trend toward developing complex sea surface and target echo simulation methods and characteristics for research is presented.

• 基于深度学习的海杂波谱参数预测与影响因素分析

  张玉石, 李笑宇, 张金鹏, 夏晓云

  该文基于不同雷达参数和海洋环境参数条件下的岸基雷达海杂波实测数据，利用深度神经网络(DNN)建模技术，建立了从多个测量条件参数出发的海杂波多普勒谱参数预测模型，实现了独立于杂波数据、基于环境特征的海杂波谱特征认知，谱频移和展宽的预测精度达90%以上。基于该预测模型，该文提出了一种基于参数循环递减认知的多普勒谱影响因素分析方法，分析了不同测量参数对海杂波多普勒谱预测的影响，得到了谱参数随主要影响因素的变化规律，结果对基于多普勒特征的海面目标检测应用具有重要意义。

• 一种改进的高分辨率SAR图像超像素CFAR舰船检测算法

  张帆, 陆圣涛, 项德良, 袁新哲

  合成孔径雷达(SAR)图像舰船目标检测一直受到学者广泛关注，恒虚警率(CFAR)检测算法作为雷达图像经典目标检测算法被广泛应用于SAR图像舰船目标检测中。然而经典CFAR检测性能容易受到相干斑噪声影响，基于滑窗的检测结果对滑窗的尺寸选择非常敏感，难以保证杂波背景中不存在目标像素，并且计算效率较低。针对上述问题，该文提出了一种新的基于超像素无窗快速CFAR的SAR图像舰船目标检测算法。首先，利用基于密度的快速噪声空间聚类(DBSCAN)超像素生成方法生成SAR图像的超像素。在SAR数据服从混合瑞利分布的假设下，定义了超像素相异度。然后利用超像素精确估计每个像素的杂波参数，即使在多目标情况下，也可以克服传统CFAR滑动窗口的缺点。此外，基于SAR图像变异系数，提出了一种基于变异系数的局部超像素对比度来优化CFAR检测，以此消除大量杂波虚警，如陆地区域人造目标。对5幅SAR图像的实验结果表明，与其他方法相比，该文方法对不同场景SAR图像海面舰船目标检测都十分稳健。

• An Improved Superpixel-based CFAR Method for High-resolution SAR Image Ship Target Detection（in English）

  Synthetic Aperture Radar (SAR) image ship target detection has attracted considerable attention. As a state-of-the-art method, the Constant False Alarm Rate (CFAR) detection algorithm is often used in SAR image ship target detection. However, the detection performance of the classical CFAR is easily affected by speckle noise. Moreover, the detection results based on the sliding window are sensitive to the size of the sliding window. Thus, ensuring that there are no target pixels in the cluttered background is difficult, which easily leads to a high computational load. This study proposes a new ship target detection method for SAR images based on fast superpixel-based non-window CFAR to solve these problems. The superpixel generation method of Density Based Spatial Clustering of Applications with Noise is used to generate superpixels for SAR images. Under the assumption that SAR data obey the Rayleigh mixture distribution, we define a superpixel dissimilarity measure. Then, the clutter parameters of each pixel are accurately estimated using superpixels, which can avoid the shortcomings of the traditional CFAR sliding window even in the case of multiple targets. A local contrast based on the Coefficient of Variation (CoV) of the SAR image is proposed to optimize the CFAR detection result, which can eliminate a large number of false alarms from man-made targets in urban areas. The experimental results of five real SAR images show that the proposed method for ship target detection in SAR images with different scenes is robust compared with other state-of-the-art methods.

• 特征图知识蒸馏引导的轻量化任意方向SAR舰船目标检测器

  陈诗琪, 王威, 占荣辉, 张军, 刘盛启

  基于有向边界框的合成孔径雷达(SAR)舰船目标检测器能输出精准的边界框，但仍存在模型计算复杂度高、推理速度慢、存储消耗大等问题，导致其难以在星载平台上部署。基于此该文提出了结合特征图和检测头分支知识蒸馏的无锚框轻量化旋转检测方法。首先，结合目标的长宽比和方向角信息提出改进高斯核，使生成的热度图能更好地刻画目标形状。然后在检测器预测头部引入前景区域增强分支，使网络更关注前景特征且抑制背景杂波的干扰。在训练轻量化网络时，将像素点间的相似度构建为热度图蒸馏知识。为解决特征蒸馏中正负样本不平衡问题，将前景注意力区域作为掩模引导网络蒸馏与目标相关的特征。另外，该文提出全局语义模块对像素进行上下文信息建模，能够结合背景知识加强目标精确表征。基于HRSID数据集的实验结果表明所提方法在模型参数仅有9.07 M的轻量化条件下，mAP能达到80.71%，且检测帧率满足实时应用需求。

• A Lightweight, Arbitrary-oriented SAR Ship Detector via Feature Map-based Knowledge Distillation（in English）

  In the Synthetic Aperture Radar (SAR) ship target detection task, the targets have a large aspect ratio and dense distribution, and they are arranged in arbitrary directions. The oriented bounding box-based detection methods can output accurate detection results. However, these methods are strongly restricted by high computational complexity, slow inference speed, and large storage consumption, which complicate their deployment on space-borne platforms. To solve the above issues, a lightweight oriented anchor-free-based detection method is proposed by combining feature map and prediction head knowledge distillation. First, we propose an improved Gaussian kernel based on the aspect ratio and angle information so that the generated heatmaps can better describe the shape of the targets. Second, the foreground region enhancement branch is introduced to make the network focus more on foreground features while suppressing the background interference. When training the lightweight student network, the similarity between pixels is treated as transferred knowledge in heatmap distillation. To tackle the imbalance between positive and negative samples in feature distillation, the foreground attention region is applied as a mask to guide the feature distillation process. In addition, a global semantic module is proposed to model the contextual information around pixels, and the background knowledge is combined to further strengthen the feature representation. Experimental results based on HRSID show that our method can achieve 80.71% mAP with only 9.07 M model parameters, and the detection frame rate meets the needs of real-time applications.

• 纹理异常感知SAR自监督学习干扰抑制方法

  韩朝赟, 岑熙, 崔嘉禾, 李亚超, 张鹏

  面对日渐复杂的电磁干扰环境，合成孔径雷达干扰抑制已成为亟须解决的难题。现有主流合成孔径雷达非参数/参数化干扰抑制方法，严重依赖干扰先验和强能量差异，存在计算复杂度高、信号损失严重等问题，难以满足对抗日益复杂的干扰的需求。针对上述问题，该文提出一种基于纹理异常感知的SAR自监督学习干扰抑制方法，利用正常雷达回波与干扰的时频域纹理差异性特征克服干扰先验的约束。首先，构建了一种干扰时频定位网络模型Location-Net，对雷达回波时频谱进行压缩重构，根据网络的重构误差对干扰进行时频定位；其次，针对干扰抑制损失问题，构建了一种信号修复神经网络模型Recovery-Net，实现对干扰抑制后回波信号损失修复。相比传统方法，所提方法克服对干扰先验的需求，可有效对抗多种复杂干扰类型，具备较强的泛化能力。基于仿真和实测数据的抗干扰处理结果，验证了所提方法对多种有源主瓣压制干扰的有效性，并通过与3种现有抗干扰方法进行对比，体现了该算法的优越性。最后，对比了所提神经网络与主流轻量化神经网络的复杂度差异，结果表明设计的两个神经网络计算复杂度更低，具备实时应用前景。

• 基于SVM的捷变频雷达密集转发干扰智能抑制方法

  杜思予, 刘智星, 吴耀君, 沙明辉, 全英汇

  密集转发干扰与雷达发射信号高度相关，兼具压制式和欺骗式干扰效果，使雷达系统难以检测到真实目标，严重威胁雷达作战能力。针对这一问题，该文提出一种基于支持向量机(SVM)的捷变频雷达密集转发干扰智能抑制方法。通过对随机样本集进行离线训练获得最优SVM模型，智能化识别并分类目标和干扰；然后，采用平滑滤波进一步抑制目标所在距离单元内的干扰信号；最后，基于压缩感知(CS)理论进行二维高分辨重构，估计出目标参数信息。仿真实验与实测数据处理结果表明，所提算法在不同场景下均能够有效抑制密集转发干扰，准确检测出真实目标。

• 基于差分特征的间歇采样转发干扰辨识与抑制方法

  盖季妤, 姜维, 张凯翔, 梁振楠, 陈新亮, 刘泉华

  间歇采样转发式干扰机通过对其接收到的雷达发射信号进行采样、存储、处理和多次转发，在雷达接收端形成逼真的假目标干扰效果。为提升上述干扰场景下的雷达探测性能，该文提出了一种新的信号差分特征提取方法，在此基础上，利用目标回波和干扰信号在差分特征空间的差异设计判决准则，从而在有效辨识并抑制干扰的同时实现目标检测。仿真结果表明：该方法干扰抑制效果显著，相比于3种典型的时频域滤波算法等效信噪比改善4.2 dB以上。

• A Method for Interrupted-Sampling Repeater Jamming Identification and Suppression Based on Differential Features（in English）

  The interrupted-sampling repeater jammer can sample, store, process and transmit part of the radar transmitter signal multiple times and the fake targets will form on the radar receiver. To improve the radar performance in the aforementioned jamming scenario, in this study, a new signal differential feature extraction method is proposed, and a judgment criterion is formulated based on the difference between the target echo and the Interrupted-Sampling Repeater Jamming (ISRJ) in the differential feature space to effectively identify and suppress the ISRJ while achieving target detection. Simulation results show that the proposed method has a remarkable ISRJ suppression performance. The equivalent signal-to-noise ratio is improved by at least 4.2 dB compared with three typical time-frequency domain filtering algorithms.

• 分布式多传感器多目标跟踪方法综述

  曾雅俊, 王俊, 魏少明, 孙进平, 雷鹏

  多传感器多目标跟踪是信息融合领域的热点问题，其通过融合多个局部传感器数据，提高目标跟踪精度和稳定性。多传感器多目标跟踪按融合体系可分为分布式、集中式、混合式3类，其中分布式融合结构对网络通信带宽要求低、可靠性和稳定性强，广泛应用于军事、民用领域。该文聚焦分布式多传感器多目标跟踪涉及的目标跟踪、传感器配准、航迹关联、数据融合4项关键技术，主要分析了各关键技术的理论原理与适用条件，重点介绍了不完整测量条件下的空间配准与航迹关联，并给出仿真结果。最后，该文总结了现有分布式多传感器多目标跟踪关键技术存在的问题，并指出了其未来发展趋势。

• Review of the Method for Distributed Multi-sensor Multi-target Tracking（in English）

  Multi-sensor multi-target tracking is a popular topic in the field of information fusion. It improves the accuracy and stability of target tracking by fusing multiple local sensor information. By the fusion system, the multi-sensor multi-target tracking is grouped into distributed fusion, centralized fusion, and hybrid fusion. Distributed fusion is widely applied in the military and civilian fields with the advantages of strong reliability, high stability, and low requirements on network communication bandwidth. Key techniques of distributed multi-sensor multi-target tracking include multi-target tracking, sensor registration, track-to-track association, and data fusion. This paper reviews the theoretical basis and applicable conditions of these key techniques, highlights the incomplete measurement spatial registration algorithm and track association algorithm, and provides the simulation results. Finally, the weaknesses of the key techniques of distributed multi-sensor multi-target tracking are summarized, and the future development trends of these key techniques are surveyed.

• 基于Raw I/Q和深度学习的射频指纹识别方法综述

  陈翔, 汪连栋, 许雄, 申绪涧, 冯蕴天

  硬件差异会形成辐射源的独有指纹，并附加在无线电信号上，利用辐射源的这一独特属性可进行射频指纹识别。在非合作条件下，由于信道环境未知、信号调制方案等先验知识匮乏，基于特征工程的射频指纹识别方法面临巨大挑战，而基于深度学习的射频指纹识别方法，尤其是能够直接处理Raw I/Q的方法表现出了很大潜力，但是该方向的研究成果较为零散，妨碍了研究者对关键问题的把握。该文首先从先验知识的利用上，对基于深度学习的射频指纹识别方法进行了分类对比，将问题聚焦到基于Raw I/Q和深度学习的射频指纹识别方法。然后，该文重点对使用Raw I/Q进行射频指纹识别的深度神经网络模型进行了分类和讨论，并对射频指纹识别相关的开源数据集、数据表示方法和数据增强方法进行了整理和归纳。最后，该文讨论了基于深度学习的射频指纹识别方法所面临的难题和值得关注的研究方向，以期对射频指纹识别的研究与应用有所帮助。

• A Review of Radio Frequency Fingerprinting Methods Based on Raw I/Q and Deep Learning（in English）

  The hardware imperfection can generate a unique fingerprint of the trasmitter, and it is attached to the radio signal. The unique attribute of transmitter can be used for Radio Frequency Fingerprinting (RFF). Due to the unknown channel conditional and the lack of prior information such as modulation scheme, the traditional method of RFF faces huge challenges to non-cooperative conditions. On the contrary, RFF methods based on Deep Learning (DL), especially those that can directly process raw I/Q, show great potential. However, the research results of this direction are scattered, which seriously hinders researchers from grasping the key issues. This paper first classifies and compares the RFF methods based on DL according to the utilization of prior knowledge, and focuses on the RFF methods based on raw I/Q and DL. Then, this paper focuses on the classification and discussion of the deep neural network model of RFF using raw I/Q, and summarizes the open source data sets, data representation methods and data augmentation methods related to RFF. Finally, this paper discusses the difficulties and research directions of the RFF based on DL, hoping to help the research and application of the RFF.

• 杂波先验数据缺失条件下基于级联优化处理的雷达波形设计方法

  张应奎, 孙国皓, 钟苏川, 余显祥

  认知雷达波形设计往往依赖于精准的杂波先验信息，当先验信息数据存在缺失时，所构建的杂波模型会严重失配，进而影响雷达对杂波的抑制能力。该文针对杂波先验数据缺失条件下的雷达波形优化问题，建立完全随机缺失机制下的点状与块状缺失场景，设计恒模与相似性约束的波形优化模型，提出基于优先级填充-强化学习级联优化的雷达波形训练算法：即采用强化学习智能体与填充算法修复后的杂波环境相交互的级联方法，以最大化信杂噪比为优化目标，通过迭代训练得到雷达最佳波形参数配置策略。最后，仿真验证不同缺失概率条件下所提算法的优越性。结果表明：相比于传统非级联优化算法，该文所提算法均可获得更优的杂波抑制性能，有效提升雷达的探测能力。