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Research Progress and Development Trend of the Multi-Illuminator-based Passive Radar （in English）
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摘要: 该文从新体制被动雷达的功能和性能优势出发，首先简要回顾了被动雷达长达80余年的研究历程；然后较为全面地介绍了相关关键技术的研究进展，包括参考信号重构、多径杂波抑制、目标检测、目标跟踪、被动雷达成像等方面；在此基础上，从系统结构、技术参数、性能指标等方面分别展示了国外(尤其是欧洲相关国家)典型被动雷达实验系统的最新研究成果，接着重点介绍了武汉大学基于多照射源的被动雷达(MIPAR)系统的研发情况，给出了不同频段(HF/VHF/UHF/L) MIPAR系统的目标探测结果，展示了MIPAR系统在远程预警及近距离高精度监视等方面的应用潜力；最后从多照射源集成化、系统配置网络化、信息处理智能化等方面总结了被动雷达的发展趋势。
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关键词:
- 被动雷达(外辐射源雷达) /
- 无源相干定位 /
- 多照射源 /
- 网络化雷达 /
- 雷达信号处理
Abstract: Given the functions and performance advantages of passive radar, this paper first reviews the research history of passive radar for more than 80 years and then examines the research progress of related key technologies, including reference signal reconstruction, multipath clutter suppression, target detection, target tracking, and passive radar imaging. On this basis, the latest research results of typical experimental systems of passive radar abroad (particularly in European countries) are presented in terms of system structures, technical parameters, and performance indices. Then this paper focuses on the Multi-Illuminator-based PAssive Radar (MIPAR) series of Wuhan University in China. The target detection results of MIPAR in different frequency bands (HF/VHF/UHF/L) are given, that show the application potential of the MIPAR system in long-range early warning and close-range high-precision monitoring. Finally, the development trends of passive radar, including the integration of multiple illuminators, system network configuration, and intelligent signal processing, are discussed. -
1. 引言
合成孔径雷达(Synthetic Aperture Radar, SAR)可以不受气候、天气、光照等条件影响，获得高分辨率雷达图像。与光学传感器相比，SAR在侦察、监视和跟踪等军事领域更具优势。然而SAR图像成像机理较为复杂，目标由较少的散射点组成而没有清晰轮廓，图像存在斑点噪声，这使得SAR飞机检测困难重重。
传统的SAR图像目标检测方法可以分为3类[1]。第1种基于单特征的方法通常利用雷达散射截面积(Radar Cross Section, RCS)信息挑选对比度较亮的部分作为候选目标。其中大多数检测方法使用恒虚警率(Constant False Alarm Rate, CFAR)算法做图像分割和候选目标定位。CFAR包含多种衍生算法，包括CA-CFAR (Cell-average CFAR)[2], SO-CFAR (Smallest of CFAR), GO-CFAR (Greatest of CFAR)[3], OS-CFAR (Order-statistic CFAR)[4]和VI-CFAR(Variability Index CFAR)[5]。CFAR具有恒虚警率和自适应阈值的特性[6–8]。然而CFAR检测器只考虑像素对比度而忽略了目标的结构信息，从而导致了目标的不精确定位。第2种是基于多特征的方法。多种特征如几何结构[9]，扩展分形(Extended Fractal, EF)[10]，小波系数[11]等可以融合起来检测目标。文献[12]中作者将梯度纹理显著图与CFAR相结合来检测停机坪上的飞机目标。综合而言，设计特征是复杂且耗时的，并且同种特征组合不一定适用于所有的场景。第3种是基于先验的方法，先验知识如成像参数、经纬度信息等需要协同加入检测流程[13]。这类方法较复杂且实际中应用较少。
随着人工智能的发展，机器学习被引入了SAR目标检测领域。支持向量机(Support Vector Machine, SVM)[14]和AdaBoost(Adaptive Boosting)[15]等常用方法在MSTAR(Moving and Stationary Target Acquisition and Recognition)[16]数据集上表现良好。虽然这些方法比传统方法性能有所提升，但它们仅适用于小样本情况，设计具有高泛化能力的特征难度较高。卷积神经网络 (Convolutional Neural Network, CNN)[17]可以自动学习结构化特征并取得较好的性能。文献[18,19]中作者使用CNN对MSTAR数据集进行分类并取得较好的效果。
本文构建了一个完整的SAR图像飞机目标检测框架。总体流程如图1所示，本文的主要工作如下。首先，提出了一种改进的显著性预检测方法，实现在大场景中多尺度粗略快速地搜索候选目标。与滑动窗方法相比，该方法有效提升了检测效率并实现了多尺度定位。然后，设计并调校了适用于SAR图像的CNN模型，实现对预检测候选目标的精确检测，实验证实本文的CNN检测精度高于其他常用的检测方法。此外，为了应对SAR数据量有限的问题，提出了4种适用于SAR图像的数据增强方法，包括平移、加噪、对比度增强和小角度旋转。实验结果表明，该检测框架在TerraSAR-X数据集上有效减小了过拟合现象的影响，显著提升了飞机目标的检测率。
图 1 SAR飞机检测框架的整体流程图Figure 1. Overall flowchart of our SAR aircraft detection framework2. 改进的显著性预检测方法原理与设计
在预检测阶段，滑动窗方法通常用于获取可能的候选目标。该方法机械地在待检测图像上，按预设步长和窗口大小，从左向右、从上向下滑动并裁切候选样本。然而，滑动窗法计算量很大，在大场景遥感图像中预检测效率较低。
为了解决上述问题，分别在训练和测试阶段前引入了一种基于显著性的预检测方法[20]，以便快速粗略地定位候选目标。文献[20]中作者首先计算每个像素的显著值并得到整幅图像的显著图，飞机切片的显著图如图2所示。相同大小的初始方形窗口被不重不漏地覆盖在显著图上。然后，迭代计算每个窗口的几何中心pc并将窗口中心移动到新的p ′c，直到pc和p ′c之间的距离小于预设值 $\delta $。迭代结束后，所有飞机目标都被窗口框定，图中还存在一些不包含真实目标的虚警。
该方法的缺点是用固定尺寸的窗口检测不同尺寸的飞机。考虑到停机坪区域停放不同尺寸飞机的情况，本文在原有算法的基础上加入多尺度预检测模块。首先不同大小窗口的单尺度预检测分别进行，然后是第1次窗口融合。融合采用非极大值抑制算法(Non-Maximum Suppression, NMS)，将包含同一目标的不同窗口融合为一个。当所有单尺度预检测结束后，将进行第2次窗口融合。
改进的显著性预检测方法的简要流程图如图3所示。所有选中的切片将被保存为候选目标图像以备后续CNN的精确检测。与滑动窗方法相比，该方法在大尺度SAR图像检测中更为高效。此外，改进后的方法因二次窗口融合，虚警率显著降低，且可以精确地定位不同尺寸的飞机目标。
图 3 改进的显著性预检测方法流程图Figure 3. Flowchart of improved saliency-based pre-detection method3. 数据增强原理与设计
深度学习在训练集较大时能够取得较好效果，但与光学图像相比SAR图像相对较少，易导致严重的过拟合。不同于常用的数据增强方法，由于SAR图像与光学图像成像原理差别较大，需要引入新的数据增强方法。本文提出适用于SAR图像的4种数据增强方法来扩充已标注数据集。4种数据增强方法如下所示。
3.1 平移
在目标不超过图像边界的条件下，对原始图像执行平移操作。假设原始图像 $P \in {{\rm{R}}^{m \times n}}$大小为m×n，则平移后的图像 $P' \in {{\rm{R}}^{m \times n}}$可以表示为：
P′i,j=Pi+x,j+y (1) 其中，(x, y)是位移尺度，(i, j)是平移后坐标。图4(a)，图4(b)给出了平移前后的目标示例。
3.2 加噪
由于特殊的成像特性，SAR图像总是带有斑点噪声。斑点噪声是白色散斑状的乘性噪声。加噪后的图像 $P' \in {{\rm{R}}^{m \times n}}$可以被表示为：
P′=P+N×P (2) 其中，P表示原始图像，N表示斑点噪声。N为均值为0方差为v的均匀分布的随机噪声。由于加噪越严重图像越模糊，必须确保噪声的强度低于预设值。图4给出了基于原始图像图4(a)的加噪示例图4(c)。
3.3 对比度增强
同一地点拍摄的SAR图像可能有不同的亮度。因此本文利用像素对比度信息进行数据增强。对比度增强可以通过非线性变换来实现，具体变换可以表示如下：
P′=P+k(I−P)×P (3) 其中，P表示原始图像，P′ 表示对比度增强后的图像。k是用户预定义的可调节因子，其值在0和1之间。 I 是与P具有相同维数的单位矩阵。此外，P和P ′ 范围均为(0, 1)。图4(a)，图4(d)给出了对比度增强的示例。
3.4 小角度旋转
雷达散射特性随着物体和传感器之间相对姿态的变化而变化。然而文献[21]中作者证明了飞机目标后向散射特性的位置和强度在至少5°内是旋转不变的。旋转坐标与原始坐标的映射关系可以表示为：
{X′=Xcosθ+YsinθY′=−Xsinθ+Ycosθ (4) 其中， $(X',Y')$和 $(X,Y\;\,)$分别表示旋转后的坐标和原始坐标。 $\theta $表示旋转角度，赋值为逆时针5°以保持旋转不变性。图4(a)，图4(e)给出了小角度旋转变换示例。
4. 卷积神经网络原理与设计
基于LeNet-5网络，改进后的网络更适用于SAR图像飞机检测。由于SAR数据较少，大量的实验表明网络超过6层会发生过拟合。如图5所示，本文的CNN网络由4个可训练参数层组成。为了减小过拟合的影响，引入了dropout方法[22]。此外，用修正线性单元(Rectified Linear Units, ReLU)在实践中能很好地应对饱和问题[23]，将其代替sigmoid函数。本文基于典型的随机梯度下降法，引入改进算法如动量法[24]以最小化损失函数。具体实现细节如下。
4.1 卷积层
在卷积层中，将输入X和一组滤波器W进行卷积，然后与偏置b相加。W表示可训练滤波器，b表示可训练偏差。最后，将上述结果传递给非线性激活函数f。公式如下：
Yi′j′=f(bk′+m∑i=1n∑j=1Wijk′Xi′+i,j′+j) (5) 其中， $f( \cdot )$是修正线性单元(ReLU)，函数由式(6)给出：
f(xij)=max{0,xij} (6) ReLU缩短了训练时间，并且在没有无监督预训练时效果较好[23]。
4.2 Max-pooling
Max-pooling在卷积层之后执行降采样操作。Max-pooling层计算出m×n局部切片内区域的最大值，公式如下：
Yi′j′=max1<i<m,1<j<nXi′+i,j′+j (7) 其中，(m, n)表示局部区域的大小，Y表示pooling操作的输出。
4.3 Softmax
Softmax分类器在输出层后对切片进行二分类。它求出每类的判别概率并选择最大值作为最终输出。Softmax函数公式如下：
Yi=exp(Xi)/exp(Xi)k∑j=1exp(Xj)k∑j=1exp(Xj) (8) 其中，Xi 表示最后隐藏层的输出，k表示类的数量，Yi 表示类i的判别概率。
4.4 实现细节
本文CNN的结构如图5所示，它由3个卷积层和3个pooling层组成。第1个卷积层的卷积核大小为5×5，并有32个输出图。相似地，第2个卷积层的卷积核尺寸也是5×5且有64个输出图。最后一个卷积层有128个输出图，卷积核大小为6×6。每个卷积层后连着2×2的Max-pooling层。输入图像切片的大小为120×120。它们在第1个卷积层后变为116×116，在第1个pooling层之后变为58×58。循环往复，Softmax层前输出两个大小为11×11的特征图。表1列出了CNN的具体结构数据。网络训练时间约30 min，测试时间约几秒钟。
表 1 CNN参数Table 1. Parameters of our CNN层结构 核结构 输出尺寸 输入层 – 120×120 C1 32@5×5 116×116 S1 2×2 58×58 C2 64@5×5 54×54 S2 2×2 27×27 C3 128@6×6 22×22 S3 2×2 11×11 5. 实验与分析
5.1 0.5 m分辨率TerraSAR-X数据集
本文使用的数据集是高分辨率TerraSAR-X卫星数据。所有数据含30张原始图像，大小约20000×20000，覆盖几个常见机场，分辨率为0.5 m×0.5 m。本文数据集包含多种类型、多种朝向的飞机。首先，基于原始图像手动标记飞机目标并保存真值文件。在手工标注中我们参考了SAR解译人员的意见，以保证样本真值的可靠性。然后将切片分为两类：1000个目标和16000个非目标。之后，随机地将样本按照4:1的比例分为训练集和测试集。图6给出了训练集的示例。可以看到，正样本包含了各种形态的飞机，负样本包含了各种复杂场景。
5.2 改进的显著性预检测方法参数设置及结果分析
在大尺度遥感图像中，滑动窗方法一般用于候选目标预检测。显著性预检测方法的性能比滑动窗方法更好。显著性预检测方法可以快速定位候选目标并显著减少虚警。通过比较两种方法后发现，给定一个大小为8000×8000的SAR图像，显著性预检测方法在42.95 s内选出1489个候选目标。相同情况下，滑动窗口方法在80.06 s内筛选出611524个候选目标。
在原始显著性预检测方法的基础上，本文提出了多尺度算法来检测不同尺寸的飞机目标。为了适应在未知场景中不同尺寸飞机的情况，对原始算法进行了基于多尺度的改进。表2表示了不同预检测方法在大小为3708×3951的SAR图像上的性能比较。Selective Search方法采用图像分割和层次算法，虽能适应不同尺度，但处理流程复杂、运算速度较慢，虚警较多，为535个，框定的飞机目标不够完整，检测框尺寸波动过于明显；显著性预检测方法计算快速，但漏检2架飞机，虚警266个；改进后的显著性预检测方法可以搜索出所有真实目标，虚警相对原始显著性方法减少58个，检测框长宽比合适，范围可以人工设置，不会出现极小或极大的窗口。Selective search方法、原始显著性预检测方法和改进后的预检测方法的实验效果示例如图7所示，可以看到改进后的显著性方法预检测性能更好。
表 2 不同预检测方法性能比较Table 2. The performance of different pre-detection methods方法 预检测 正确率(%) 候选目标 个数 预检测 时间(s) Selective search 100 569 47.50 原始显著性预检测 94.12 298 6.07 改进的显著性预检测 100 242 10.67 
图 7 Selective search方法、原始显著性预检测方法和改进后的预检测方法Figure 7. Comparison among the Selective search method, the basic saliency-based method and improved saliency-based method5.3 数据增强参数设置及结果分析
在数据增强阶段，800个原始训练正样本首先按照0.01的方差斑点加噪，然后顺时针旋转5°，再向上平移5个像素，最后以k=0.5对比度增强。按这种方式，原始的训练正样本扩展16倍至12800个，同样地，原始训练负样本由200个扩展到3200个。
为了比较不同数据增强方法，本文对每一种增强方法做了单独实验，同时与4种增强方法合用进行对比，实验结果如表3所示。
表 3 不同数据增强方法的检测正确率Table 3. Accuracy rates of CNN with different augmentation methods操作 检测正确率(%) 原始 86.33 平移 92.01 斑点加噪 93.74 对比度增强 93.64 小角度旋转 92.76 综合4种增强方法 96.36 没有施加任何数据增强方法时，CNN的检测正确率为86.33%。分析可得：每种数据增强方法对最后检测正确率的提升都有作用，斑点加噪的提升作用更大。当把4种增强方法结合起来时，检测正确率达到了96.36%，超过了单独使用每种数据增强方法的性能。
5.4 卷积神经网络参数设置及结果分析
在训练前，将具有不同尺寸的候选目标调整为统一尺寸120×120。本文做了大量实验以获取最佳参数。当基本学习率为0.01, momentum为0.9, batchsize大小为50，迭代次数为5000时检测性能最佳。训练集包含25600个样本，测试集包含6400个样本。CNN部分的实验在具有Tesla k40M GPU和251 GB存储器的CAFFE框架上实现。实验的其余部分在具有32 GB存储器的3.1 GHz CPU上实现。
表4、表5、表6分别探究了不同网络结构、不同卷积核个数和不同卷积核大小对检测性能的影响。实验发现，分别改变网络层数、卷积核个数、卷积核大小后检测正确率均低于本文方法的性能指标。
表 4 不同网络层数的检测正确率比较Table 4. Accuracy rates of CNN with different number of layers网络结构 检测正确率(%) C1: 32@5×5, S1, C2: 64@5×5, S2, C3: 128@6×6, S3, C4: 256@6×6, S4 95.99 C1: 32@5×5, S1, C2: 64@5×5, S2, C3: 128@6×6, S3 (本文) 96.36 C1: 32@5×5, S1, C2: 64@5×5, S2 93.70 表 5 不同卷积核个数的检测正确率比较Table 5. Accuracy rates of CNN with different number of kernels网络结构 检测正确率(%) C1: 16@5×5, S1, C2: 32@5×5, S2, C3: 64@6×6, S3 93.93 C1: 32@5×5, S1, C2: 64@5×5, S2, C3: 128@6×6, S3 (本文) 96.36 C1: 64@5×5, S1, C2: 128@5×5, S2, C3: 256@6×6, S3 95.05 表 6 不同卷积核大小的检测正确率比较Table 6. Accuracy rates of CNN with different size of kernels网络结构 检测正确率(%) C1: 32@3×3, S1, C2: 64@3×3, S2, C3: 128@3×3, S3 95.96 C1: 32@5×5, S1, C2: 64@5×5, S2, C3: 128@6×6, S3 (本文) 96.36 C1: 32@5×5, S1, C2: 64@5×5, S2, C3: 128@5×5, S3 96.16 为了验证方法的高效性，将本文的CNN网络与在SAR图像处理中广泛使用的其它机器学习方法(例如SVM和AdaBoost)作了对比。SVM使用径向基函数(Radial Basis Function, RBF)作为核函数，AdaBoost使用决策树作为弱分类器。方向梯度直方图(Histogram of Oriented Gradient, HOG)加SVM方法也加入了比较。表7列出了各种常用方法的检测正确率。实验证实，本文CNN的检测正确率达到96.36%，并且优于其他常用的SAR飞机检测方法。
表 7 不同方法在同一数据集上的平均检测率Table 7. Average detection rates of different methods on the same dataset方法 检测正确率(%) CNN 96.36 SVM 92.64 HOG+SVM 93.79 AdaBoost 92.28 不同方法的ROC曲线如图8所示，可以看到我们的CNN方法与横轴所围面积最大，检测性能最好，其余由高到底依次是HOG+SVM, SVM和AdaBoost方法。
当把上述所有算法合为一体时，最终得到SAR图像飞机目标的完整检测算法框架。图9表示了该检测方法在某停机坪区域的检测结果。在这样的复杂场景下，虽然存在少量虚警但并无漏警。总之，本文的飞机检测框架在大场景SAR图像中可以取得较为理想的效果。
6. 结论
本文构建了一个完整的SAR飞机目标检测算法框架。首先，提出了一种改进的显著性预检测方法，实现在大规模SAR场景中粗略快速地定位候选飞机目标。实验证实该方法与滑动窗方法相比更为高效，改进后多尺度的加入提升了对待测场景的适应性。然后，设计并调优了含4个权重层的CNN网络，实现对候选目标的精确检测并得到最终的检测结果。此外，为了丰富训练集并防止过拟合，提出了4种数据增强方法。具体包括平移、斑点加噪、对比度增强和小角度旋转。实验结果证实，本文的飞机检测算法框架取得了96.36%的检测正确率，并优于其他常用的SAR飞机检测方法。
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图 3 HF波段被动雷达探测结果与目标ADS-B信息对比
Figure 3. Comparison between HF-band passive radar detection results and ADS-B information
图 7 基于多照射源的被动雷达探测结果
Figure 7. Detection results of the multi-illuminator passive radar
图 8 智能检测算法与恒虚警检测算法结果对比
Figure 8. Comparison between the detection results of the intelligent detector and the constant false alarm rate detector
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[1] GRIFFITH H D and BAKER C J. Passive coherent location radar systems. Part 1: Performance prediction[J]. IEE Proceedings – Radar, Sonar and Navigation, 2005, 152(3): 153–159. doi: 10.1049/ip-rsn:20045082. [2] BAKER C J, GRIFFITHS H D, and PAPOUTSIS I. Passive coherent location radar systems. Part 2: Waveform properties[J]. IEE Proceedings – Radar, Sonar and Navigation, 2005, 152(3): 160–168. doi: 10.1049/ip-rsn:20045083. [3] 万显荣. 基于低频段数字广播电视信号的外辐射源雷达发展现状与趋势[J]. 雷达学报, 2012, 1(2): 109–123. doi: 10.3724/SP.J.1300.2012.20027.WAN Xianrong. An overview on development of passive radar based on the low frequency band digital broadcasting and TV signals[J]. Journal of Radars, 2012, 1(2): 109–123. doi: 10.3724/SP.J.1300.2012.20027. [4] 宋杰, 何友, 蔡复青, 等. 基于非合作雷达辐射源的无源雷达技术综述[J]. 系统工程与电子技术, 2009, 31(9): 2151–2156, 2180. doi: 10.3321/j.issn:1001-506X.2009.09.028.SONG Jie, HE You, CAI Fuqing, et al. Overview of passive radar technology based on non-cooperative radar illuminator[J]. Systems Engineering and Electronics, 2009, 31(9): 2151–2156, 2180. doi: 10.3321/j.issn:1001-506X.2009.09.028. [5] KUSCHEL H, CRISTALLINI D, and OLSEN K E. Tutorial: Passive radar tutorial[J]. IEEE Aerospace and Electronic Systems Magazine, 2019, 34(2): 2–19. doi: 10.1109/MAES.2018.160146. [6] 郑恒, 王俊, 江胜利, 等. 外辐射源雷达[M]. 北京: 国防工业出版社, 2017: 1–10.ZHENG Heng, WANG Jun, JIANG Shengli, et al. Passive Bistatic Radar[M]. Beijing: National Defense Industry Press, 2017: 1–10. [7] 吕晓德, 仲利华, 刘忠胜, 等. 无源相参雷达系统 —原理、信号处理及设计[M]. 北京: 科学出版社, 2019: 1–22.LV Xiaode, ZHONG Lihua, LIU Zhongsheng, et al. Passive Coherent Radar System—Principle, Signal Processing and Design[M]. Beijing: Science Press, 2019: 1–22. [8] GRIFFITHS H and WILLIS N. Klein Heidelberg—the first modern bistatic radar system[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(4): 1571–1588. doi: 10.1109/TAES.2010.5595580. [9] GRIFFITHS H D and LONG N R W. Television-based bistatic radar[J]. IEE Proceedings F - Communications, Radar and Signal Processing, 1986, 133(7): 649–657. doi: 10.1049/ip-f-1.1986.0104. [10] HOWLAND P E. Target tracking using television-based bistatic radar[J]. IEE Proceedings - Radar, Sonar and Navigation, 1999, 146(3): 166–174. doi: 10.1049/ip-rsn:19990322. [11] HOWLAND P E, MAKSIMIUK D, and REITSMA G. FM radio based bistatic radar[J]. IEE Proceedings - Radar, Sonar and Navigation, 2005, 152(3): 107–115. doi: 10.1049/ip-rsn:20045077. [12] SAINI R and CHERNIAKOV M. DTV signal ambiguity function analysis for radar application[J]. IEE Proceedings - Radar, Sonar and Navigation, 2005, 152(3): 133–142. doi: 10.1049/ip-rsn:20045067. [13] POULLIN D. Passive detection using digital broadcasters (DAB, DVB) with COFDM modulation[J]. IEE Proceedings - Radar, Sonar and Navigation, 2005, 152(3): 143–152. doi: 10.1049/ip-rsn:20045017. [14] 苏卫民, 顾红, 张先义. 基于外辐射源的雷达目标探测与跟踪技术研究[J]. 现代雷达, 2005, 27(4): 19–22. doi: 10.3969/j.issn.1004-7859.2005.04.006.SU Weimin, GU Hong, and ZHANG Xianyi. A study on radar target detection and tracking technology based on opportunity transmitter[J]. Modern Radar, 2005, 27(4): 19–22. doi: 10.3969/j.issn.1004-7859.2005.04.006. [15] 王俊, 张守宏, 保铮. 基于外照射的无源相干雷达系统及其关键问题[J]. 电波科学学报, 2005, 20(3): 381–385. doi: 10.3969/j.issn.1005-0388.2005.03.021.WANG Jun, ZHANG Shouhong, and BAO Zheng. Study on the external illuminator based passive coherent radar experimental system[J]. Chinese Journal of Radio Science, 2005, 20(3): 381–385. doi: 10.3969/j.issn.1005-0388.2005.03.021. [16] COLEMAN C and YARDLEY H. Passive bistatic radar based on target illuminations by digital audio broadcasting[J]. IET Radar, Sonar & Navigation, 2008, 2(5): 366–375. doi: 10.1049/iet-rsn:20080019. [17] 杨广平. 外辐射源雷达关键技术研究[J]. 现代雷达, 2008, 30(8): 5–9. doi: 10.3969/j.issn.1004-7859.2008.08.002.YANG Guangping. A study on key technology of passive radar[J]. Modern Radar, 2008, 30(8): 5–9. doi: 10.3969/j.issn.1004-7859.2008.08.002. [18] 万显荣, 邵启红, 柯亨玉, 等. 基于数字调幅广播的无源双基地地波雷达[J]. 雷达科学与技术, 2009, 7(6): 401–405. doi: 10.3969/j.issn.1672-2337.2009.06.001.WAN Xianrong, SHAO Qihong, KE Hengyu, et al. HF passive bistatic surface wave radar based on DRM digital AM broadcast[J]. Radar Science and Technology, 2009, 7(6): 401–405. doi: 10.3969/j.issn.1672-2337.2009.06.001. [19] 赵兴浩, 陶然. 无源雷达GSM信号模糊函数研究[J]. 现代雷达, 2004, 26(2): 31–34. doi: 10.3969/j.issn.1004-7859.2004.02.009.ZHAO Xinghao and TAO Ran. Ambiguity function of GSM signal for passive radar[J]. Modern Radar, 2004, 26(2): 31–34. doi: 10.3969/j.issn.1004-7859.2004.02.009. [20] ZEMMARI R, DAUN M, and NICKEL U. Maritime surveillance using GSM passive radar[C]. The 13th International Radar Symposium (IRS), Warsaw, Poland, 2012: 76–82. doi: 10.1109/IRS.2012.6233293. [21] COLONE F, FALCONE P, BONGIOANNI C, et al. WiFi-based passive bistatic radar: Data processing schemes and experimental results[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(2): 1061–1079. doi: 10.1109/TAES.2012.6178049. [22] MA Hui, ANTONIOU M, STOVE A G, et al. Maritime moving target localization using passive GNSS-Based multistatic radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(8): 4808–4819. doi: 10.1109/TGRS.2018.2838682. [23] VEREMYEV V I, VOROBEV E N, and KOKORINA Y V. Feasibility study of air target detection by passive radar using satellite-based transmitters[C]. 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, Saint Petersburg and Moscow, Russia, 2019: 154–157. doi: 10.1109/EIConRus.2019.8656630. [24] SANTI F, PIERALICE F, and PASTINA D. Joint detection and localization of vessels at sea with a GNSS-based multistatic radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(8): 5894–5913. doi: 10.1109/TGRS.2019.2902938. [25] PASTINA D, SANTI F, PIERALICE F, et al. Passive radar imaging of ship targets with GNSS signals of opportunity[J]. IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2020.3005306. [26] WANG Yasen, BAO Qinglong, WANG Dinghe, et al. An experimental study of passive bistatic radar using uncooperative radar as a transmitter[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(9): 1868–1872. doi: 10.1109/LGRS.2015.2432574. [27] SONG Jie, CAI Fuqing, ZHANG Caisheng, et al. Experimental results of maritime moving target detection based on passive bistatic radar using non-cooperative radar illuminators[J]. The Journal of Engineering, 2019, 2019(20): 6763–6766. doi: 10.1049/joe.2019.0586. [28] 中华人民共和国国家质量监督检验检疫总局, 中国国家标准化管理委员会. GB 20600-2006 数字电视地面广播传输系统帧结构、信道编码和调制[S]. 北京: 中国标准出版社, 2007.General Administration of the People’s Republic of China Quality Supervision and Quarantine, National Standardization Administration of China. GB 20600-2006 Framing structure, channel coding and modulation for digital television terrestrial broadcasting system[S]. Beijing: China Standard Press, 2007. [29] 国家广播电影电视总局. GY/T 220.1-2006 移动多媒体广播 第1部分: 广播信道帧结构、信道编码和调制[S]. 中国移动多媒体标准, 2006.State Administration of Radio, Film and Television. GY/T 220.1-2006 Mobile multimedia broadcasting part 1: Framing structure channel coding and modulation for broadcasting channel[S]. China Mobile Multimedia Broadcasting Standard, 2006. [30] European Telecommunication Standards Institute. ES 201 980 v3.1.1-Digital Radio Mondiale (DRM) System Specification[S]. 2009. [31] 王俊, 牛溢华. 基于多电视台的两种无源雷达成像算法[J]. 系统工程与电子技术, 2007, 29(8): 1263–1267. doi: 10.3321/j.issn:1001-506x.2007.08.012.WANG Jun and NIU Yihua. Two algorithms for passive radar imaging based on multiple television stations[J]. Systems Engineering and Electronics, 2007, 29(8): 1263–1267. doi: 10.3321/j.issn:1001-506x.2007.08.012. [32] 高志文, 陶然, 单涛. DVB-T辐射源雷达信号模糊函数的副峰分析与抑制[J]. 电子学报, 2008, 36(3): 505–509. doi: 10.3321/j.issn:0372-2112.2008.03.018.GAO Zhiwen, TAO Ran, and SHAN Tao. Side peaks analysis and suppression of DVB-T signal ambiguity function for passive radar[J]. Acta Electronica Sinica, 2008, 36(3): 505–509. doi: 10.3321/j.issn:0372-2112.2008.03.018. [33] 高志文, 陶然, 单涛. 外辐射源雷达互模糊函数的两种快速算法[J]. 电子学报, 2009, 37(3): 669–672. doi: 10.3321/j.issn:0372-2112.2009.03.044.GAO Zhiwen, TAO Ran, and SHAN Tao. Two fast algorithms of cross-ambiguity function for passive radar[J]. Acta Electronica Sinica, 2009, 37(3): 669–672. doi: 10.3321/j.issn:0372-2112.2009.03.044. [34] 关欣, 胡东辉, 仲利华, 等. 一种高效的外辐射源雷达高径向速度目标实时检测方法[J]. 电子与信息学报, 2013, 35(3): 581–588. doi: 10.3724/SP.J.1146.2012.00903.GUAN Xin, HU Donghui, ZHONG Lihua, et al. An effective real-time target detection algorithm for high radial speed targets in passive radar[J]. Journal of Electronics & Information Technology, 2013, 35(3): 581–588. doi: 10.3724/SP.J.1146.2012.00903. [35] 关欣, 仲利华, 胡东辉, 等. 一种基于RSPWVD-Hough变换的无源雷达多普勒展宽补偿方法[J]. 雷达学报, 2013, 2(4): 430–438. doi: 10.3724/SP.J.1300.2013.13073.GUAN Xin, ZHONG Lihua, HU Donghui, et al. A compensation algorithm based on RSPWVD-Hough transform for Doppler expansion in passive radar[J]. Journal of Radars, 2013, 2(4): 430–438. doi: 10.3724/SP.J.1300.2013.13073. [36] 唐慧, 万显荣, 陈伟, 等. 数字地面多媒体广播外辐射源雷达目标探测实验研究[J]. 电子与信息学报, 2013, 35(3): 575–580. doi: 10.3724/SP.J.1146.2012.00939.TANG Hui, WAN Xianrong, CHEN Wei, et al. Experimentation on target detection with passive radar based on Digital Terrestrial Multimedia Broadcasting[J]. Journal of Electronics & Information Technology, 2013, 35(3): 575–580. doi: 10.3724/SP.J.1146.2012.00939. [37] WAN Xianrong, YI Jianxin, ZHAO Zhixin, et al. Experimental research for CMMB-Based passive radar under a multipath environment[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(1): 70–85. doi: 10.1109/TAES.2013.120737. [38] MA Yahui, SHAN Tao, ZHANG Y D, et al. A novel two-dimensional sparse-weight NLMS filtering scheme for passive bistatic radar[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(5): 676–680. doi: 10.1109/LGRS.2016.2535173. [39] BACZYK M K and MALANOWSKI M. Reconstruction of the reference signal in DVB-T-based passive radar[J]. International Journal of Electronics and Telecommunications, 2011, 57(1): 43–48. doi: 10.2478/v10177-011-0006-y. [40] SEARLE S, HOWARD S, and PALMER J. Remodulation of DVB-T signals for use in passive bistatic radar[C]. 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), Pacific Grove, USA, 2010: 1112–1116. doi: 10.1109/ACSSC.2010.5757576. [41] MAHFOUDIA O, HORLIN F, and NEYT X. Optimum reference signal reconstruction for DVB-T based passive radars[C]. 2017 IEEE Radar Conference (RadarConf), Seattle, USA, 2017: 1446–1449. doi: 10.1109/RADAR.2017.7944411. [42] MAHFOUDIA O, HORLIN F, and NEYT X. Performance analysis of the reference signal reconstruction for DVB-T passive radars[J]. Signal Processing, 2019, 158: 26–35. [43] BOK D. Reconstruction and reciprocal filter of OFDM waveforms for DVB-T2 based passive radar[C]. 2018 International Conference on Radar (RADAR), Brisbane, Australia, 2018: 1–6. [44] O’HAGAN D W, SETSUBI M, and PAINE S. Signal reconstruction of DVB-T2 signals in passive radar[C]. 2018 IEEE Radar Conference (RadarConf), Oklahoma, USA, 2018: 1111–1116. doi: 10.1109/RADAR.2018.8378717. [45] 万显荣, 岑博, 易建新, 等. 中国移动多媒体广播外辐射源雷达参考信号获取方法研究[J]. 电子与信息学报, 2012, 34(2): 338–343. doi: 10.3724/SP.J.1146.2011.00572.WAN Xianrong, CEN Bo, YI Jianxin, et al. Reference signal extraction methods for CMMB-based passive bistatic radar[J]. Journal of Electronics & Information Technology, 2012, 34(2): 338–343. doi: 10.3724/SP.J.1146.2011.00572. [46] WAN Xianrong, WANG Junfang, HONG Sheng, et al. Reconstruction of reference signal for DTMB-based passive radar systems[C]. 2011 IEEE CIE International Conference on Radar, Chengdu, China, 2011: 165–168. doi: 10.1109/CIE-Radar.2011.6159501. [47] ZHANG Xun, YI Jianxin, WAN Xianrong, et al. Reference signal reconstruction under oversampling for DTMB-based passive radar[J]. IEEE Access, 2020, 8: 74024–74038. doi: 10.1109/ACCESS.2020.2986589. [48] SCHWARK C and HECKENBACH J. Multi-sensor reference diversity for improved OFDM signal reconstruction[C]. 2017 IEEE Radar Conference (RadarConf), Seattle, USA, 2017: 1446–1449. doi: 10.1109/RADAR.2017.7944434. [49] BERTHILLOT C, SANTORI A, RABASTE O, et al. BEM reference signal estimation for an airborne passive radar antenna array[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(6): 2833–2845. doi: 10.1109/TAES.2017.2716458. [50] GUO Shuai, WANG Jun, MA Hui, et al. Modified blind equalization algorithm based on cyclostationarity for contaminated reference signal in airborne PBR[J]. Sensors, 2020, 20(3): 788. doi: 10.3390/s20030788. [51] PALMARINI C, MARTELLI T, COLONE F, et al. Disturbance removal in passive radar via sliding extensive cancellation algorithm (ECA-S)[C]. 2015 IEEE Radar Conference, Johannesburg, South Africa, 2015: 162–167. doi: 10.1109/RadarConf.2015.7411873. [52] YI Jianxin, WAN Xianrong, LI Deshi, et al. Robust clutter rejection in passive radar via generalized subband cancellation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(4): 1931–1946. doi: 10.1109/TAES.2018.2805228. [53] 赵志欣, 万显荣, 邵启红, 等. DRM无源雷达多径杂波的分载波空域抑制[J]. 华中科技大学学报: 自然科学版, 2012, 40(3): 13–17.ZHAO Zhixin, WAN Xianrong, SHAO Qihong, et al. Multipath clutter suppression by spatial filtering on each carrier in DRM-based passive radar[J]. Journal of Huazhong University of Science and Technology: Natural Science Edition, 2012, 40(3): 13–17. [54] 易建新, 万显荣, 赵志欣, 等. 单频网CP-OFDM信号外辐射源雷达的分载波杂波抑制方法(英文)[J]. 雷达学报, 2013, 2(1): 1–13. doi: 10.3724/SP.J.1300.2013.13030.YI Jianxin, WAN Xianrong, ZHAO Zhixin, et al. Subcarrier-based processing for clutter rejection in CP-OFDM signal-based passive radar using SFN configuration[J]. Journal of Radars, 2013, 2(1): 1–13. doi: 10.3724/SP.J.1300.2013.13030. [55] LIU Yuqi, YI Jianxin, WAN Xianrong, et al. Evaluation of clutter suppression in CP-OFDM-based passive radar[J]. IEEE Sensors Journal, 2019, 19(14): 5572–5586. doi: 10.1109/JSEN.2019.2907660. [56] 万显荣, 刘玉琪, 程丰, 等. 基于信道分段平滑的外辐射源雷达非平稳杂波抑制方法[J]. 电子与信息学报, 2020, 42(1): 132–139. doi: 10.11999/JEIT190754.WAN Xianrong, LIU Yuqi, CHENG Feng, et al. Nonstationary clutter suppression method for passive radar based on channel segmentation and smoothing[J]. Journal of Electronic & Information Technology, 2020, 42(1): 132–139. doi: 10.11999/JEIT190754. [57] 刘玉琪, 万显荣, 易建新, 等. 基于信道多普勒特征的外辐射源雷达杂波抑制方法[J/OL]. 系统工程与电子技术. http://kns.cnki.net/kcms/detail/11.2422.TN.20201014.1325.020.html, 2020.LIU Yuqi, WAN Xianrong, YI Jianxin, et al. Clutter suppression method for passive radar based on channel Doppler characteristic[J/OL]. Journal of Electronic and Information Technology. http://kns.cnki.net/kcms/detail/11.2422.TN.20201014.1325.020.html, 2020. [58] CHABRIEL G, BARRÈRE J, GASSIER G, et al. Passive covert radars using CP-OFDM signals. A new efficient method to extract targets echoes[C]. 2014 Radar Conference, Lille, France, 2014: 1–6. doi: 10.1109/RADAR.2014.7060382. [59] FANG Liang, WAN Xianrong, FANG Gao, et al. Passive detection using orthogonal frequency division multiplex signals of opportunity without multipath clutter cancellation[J]. IET Radar, Sonar & Navigation, 2016, 10(3): 516–524. doi: 10.1049/iet-rsn.2015.0238. [60] FABRIZIO G, COLONE F, LOMBARDO P, et al. Adaptive beamforming for high-frequency over-the-horizon passive radar[J]. IET Radar, Sonar & Navigation, 2009, 3(4): 384–405. doi: 10.1049/iet-rsn.2008.0159. [61] 吴海洲, 陶然, 单涛. 基于DTTB照射源的无源雷达直达波干扰抑制[J]. 电子与信息学报, 2009, 31(9): 2033–2038.WU Haizhou, TAO Ran, and SHAN Tao. Direct-path interference suppression for passive radar based on DTTB illuminator[J]. Journal of Electronics & Information Technology, 2009, 31(9): 2033–2038. [62] TAO R, WU H Z, and SHAN T. Direct-path suppression by spatial filtering in digital television terrestrial broadcasting-based passive radar[J]. IET Radar, Sonar & Navigation, 2010, 4(6): 791–805. doi: 10.1049/iet-rsn.2009.0138. [63] BROWN J, WOODBRIDGE K, GRIFFITHS H, et al. Passive bistatic radar experiments from an airborne platform[J]. IEEE Aerospace and Electronic Systems Magazine, 2012, 27(11): 50–55. doi: 10.1109/MAES.2012.6380826. [64] 梁龙, 万显荣, 程丰, 等. 机载外辐射源雷达杂波模型及特性分析[J]. 电波科学学报, 2014, 29(4): 595–600. doi: 10.13443/j.cjors.2013080601.LIANG Long, WAN Xianrong, CHENG Feng, et al. Modeling and characteristics analysis of clutter for airborne passive radar[J]. Chinese Journal of Radio Science, 2014, 29(4): 595–600. doi: 10.13443/j.cjors.2013080601. [65] 万显荣, 梁龙, 但阳鹏, 等. 移动平台外辐射源雷达实验研究[J]. 电波科学学报, 2015, 30(2): 383–390. doi: 10.13443/j.cjors.2014042301.WAN Xianrong, LIANG Long, DAN Yangpeng, et al. Experimental research of passive radar on moving platform[J]. Chinese Journal of Radio Science, 2015, 30(2): 383–390. doi: 10.13443/j.cjors.2014042301. [66] PALMER J, UMMENHOFER M, SUMMERS A, et al. Receiver platform motion compensation in passive radar[J]. IET Radar, Sonar & Navigation, 2017, 11(6): 922–931. doi: 10.1049/iet-rsn.2016.0516. [67] YANG Pengcheng, LYU X D, CHAI Zhihai, et al. Clutter cancellation along the clutter ridge for airborne passive radar[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(6): 951–955. doi: 10.1109/LGRS.2017.2689076. [68] WOJACZEK P, COLONE F, CRISTALLINI D, et al. Reciprocal-filter-based STAP for passive radar on moving platforms[J].IEEE Transactions on Aerospace and Electronic Systems, 2019, 55(2): 967–988. doi: 10.1109/TAES.2018.2867688. [69] BLASONE G P, COLONE F, LOMBARDO P, et al. A two-stage approach for direct signal and clutter cancellation in passive radar on moving platforms[C]. 2019 IEEE Radar Conference (RadarConf), Boston, USA, 2019: 1–6. doi: 10.1109/RADAR.2019.8835704. [70] BLASONE G P, COLONE F, LOMBARDO P, et al. Passive radar DPCA schemes with adaptive channel calibration[J]. IEEE Transactions on Aerospace and Electronic Systems, 2020, 56(5): 4014–4034. doi: 10.1109/TAES.2020.2987478. [71] VISWANATHAN R and VARSHNEY P K. Distributed detection with multiple sensors Part I. Fundamentals[J]. Proceedings of the IEEE, 1997, 85(1): 54–63. doi: 10.1109/5.554208. [72] TAO Ran, GAO Zhiwen, and WANG Yue. Side peaks interference suppression in DVB-T based passive radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(4): 3610–3619. doi: 10.1109/TAES.2012.6324746. [73] HACK D E, PATTON L K, HIMED B, et al. Detection in passive MIMO radar networks[J]. IEEE Transactions on Signal Processing, 2014, 62(11): 2999–3012. doi: 10.1109/TSP.2014.2319776. [74] CUI Guolong, LIU Jun, LI Hongbin, et al. Signal detection with noisy reference for passive sensing[J]. Signal Processing, 2015, 108: 389–399. doi: 10.1016/j.sigpro.2014.09.034. [75] LIU Jun, LI Hongbin, and HIMED B. On the performance of the cross-correlation detector for passive radar applications[J]. Signal Processing, 2015, 113: 32–37. doi: 10.1016/j.sigpro.2015.01.006. [76] ZHANG Xin, LI Hongbin, LIU Jun, et al. Joint delay and Doppler estimation for passive sensing with direct-path interference[J]. IEEE Transactions on Signal Processing, 2016, 64(3): 630–640. doi: 10.1109/TSP.2015.2488584. [77] BIALKOWSKI K S, CLARKSON I V L, and HOWARD S D. Generalized canonical correlation for passive multistatic radar detection[C]. 2011 IEEE Statistical Signal Processing Workshop (SSP), Nice, France, 2011: 417–420. doi: 10.1109/SSP.2011.5967719. [78] ZAIMBASHI A, DERAKHTIAN M, and SHEIKHI A. GLRT-based CFAR detection in passive bistatic radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(1): 134–159. doi: 10.1109/TAES.2013.6404095. [79] LIU Jun, LI Hongbin, and HIMED B. Two target detection algorithms for passive multistatic radar[J]. IEEE Transactions on Signal Processing, 2014, 62(22): 5930–5939. doi: 10.1109/TSP.2014.2359637. [80] HACK D E, PATTON L K, HIMED B, et al. Centralized passive MIMO radar detection without direct-path reference signals[J]. IEEE Transactions on Signal Processing, 2014, 62(11): 3013–3023. doi: 10.1109/TSP.2014.2320462. [81] GAO Yongchan, LI Hongbin, and HIMED B. Knowledge-aided range-spread target detection for distributed MIMO radar in nonhomogeneous environments[J]. IEEE Transactions on Signal Processing, 2017, 65(3): 617–627. doi: 10.1109/TSP.2016.2625266. [82] GOGINENI S, SETLUR P, and RANGASWAMY M, et al. Random matrix theory inspired passive bistatic radar detection of low-rank signals[C]. 2015 IEEE Radar Conference (RadarConf), Arlington, USA, 2015: 1656–1659. doi: 10.1109/RADAR.2015.7131264. [83] SETLUR P, GOGINENI S, and RANGASWAMY M. Spectral characterizations of structured big data covariance matrices[C]. 2017 IEEE Radar Conference (RadarConf), Seattle, USA, 2017: 1745–1750. doi: 10.1109/RADAR.2017.7944489. [84] GOGINENI S, SETLUR P, RANGASWAMY M, et al. Passive radar detection with noisy reference channel using principal subspace similarity[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(1): 18–36. doi: 10.1109/TAES.2017.2730998. [85] GROSSI E, LOPS M, and VENTURINO L. A novel dynamic programming algorithm for track-before-detect in radar systems[J]. IEEE Transactions on Signal Processing, 2013, 61(10): 2608–2619. doi: 10.1109/TSP.2013.2251338. [86] ZHANG Jiancheng, SU Tao, ZHENG Jibin, et al. Novel fast coherent detection algorithm for radar maneuvering target with jerk motion[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(5): 1792–1803. doi: 10.1109/JSTARS.2017.2651156. [87] WANG Hui, YI Jianxin, WAN Xianrong, et al. Greedy algorithm- based track-before-detect in radar systems[J]. IEEE Sensors Journal, 2018, 18(17): 7158–7165. doi: 10.1109/JSEN.2018.2853188. [88] WANG Hui, YI Jianxin, and WAN Xianrong. A fast coherent integration algorithm for maneuvering target detection[J]. IEEE Sensors Journal, 2019, 19(12): 4560–4570. doi: 10.1109/JSEN.2019.2899455. [89] COLONE F and LOMBARDO P. Polarimetric passive coherent location[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(2): 1079–1097. doi: 10.1109/TAES.2014.130775. [90] COLONE F and LOMBARDO P. Non-coherent adaptive detection in passive radar exploiting polarimetric and frequency diversity[J]. IET Radar, Sonar & Navigation, 2016, 10(1): 15–23. doi: 10.1049/iet-rsn.2015.0104. [91] FILIPPINI F and COLONE F. A practical approach to polarimetric adaptive target detection in passive radar[C]. 2017 International Conference on Radar Systems, Belfast, UK, 2017: 1–6. doi: 10.1049/cp.2017.0420. [92] ZAIMBASHI A, DERAKHTIAN M, and SHEIKHI A. Invariant target detection in multiband FM-based passive bistatic radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(1): 720–736. doi: 10.1109/TAES.2013.120248. [93] MARTELLI T, COLONE F, TILLI E, et al. Multi-frequency target detection techniques for DVB-T based passive radar sensors[J]. Sensors, 2016, 16(10): 1594. doi: 10.3390/s16101594. [94] MARTELLI T, COLONE F, TILLI E, et al. Maritime surveillance via multi-frequency DVB-T based passive radar[C]. 2017 IEEE Radar Conference (RadarConf), Seattle, USA, 2017: 540–545. doi: 10.1109/RADAR.2017.7944262. [95] YI Jianxin, WAN Xianrong, LEUNG H, et al. MIMO passive radar tracking under a single frequency network[J]. IEEE Journal of Selected Topics in Signal Processing, 2015, 9(8): 1661–1671. doi: 10.1109/JSTSP.2015.2464188. [96] CHOI S, CROUSE D, WILLETT P, et al. Multistatic target tracking for passive radar in a DAB/DVB network: Initiation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(3): 2460–2469. doi: 10.1109/TAES.2015.130270. [97] CHOI S, CROUSE D F, WILLETT P, et al. Approaches to Cartesian data association passive radar tracking in a DAB/DVB network[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(1): 649–663. doi: 10.1109/TAES.2013.120431. [98] LI Xiaohua, BAUM M, WILLETT P, et al. Evaluation of the PMHT approach for passive radar tracking with unknown transmitter associations[C]. The 17th International Conference on Information Fusion, Salamanca, Spain, 2014: 1–7. [99] LI Xiaohua, ZHAO Chenxu, LU Xiaofeng, et al. DA-PMHT for multistatic passive radar multitarget tracking in dense clutter environment[J]. IEEE Access, 2019, 7: 49316–49326. doi: 10.1109/ACCESS.2019.2907789. [100] SHI Yifang and SONG T L. Sequential processing JIPDA for multitarget tracking in clutter using multistatic passive radar[C]. The 19th International Conference on Information Fusion, Heidelberg, Germany, 2016: 1–8. [101] STINCO P, GRECO M S, GINI F, et al. ComRadE: Cognitive passive tracking in symbiotic IEEE 802.22 systems[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(2): 1023–1034. doi: 10.1109/TAES.2017.2667498. [102] STINCO P, GRECO M S, and GINI F. Spectrum sensing and sharing for cognitive radars[J]. IET Radar, Sonar & Navigation, 2016, 10(3): 595–602. doi: 10.1049/iet-rsn.2015.0372. [103] KUSCHEL H, UMMENHOFER M, LOMBARDO P, et al. Passive radar components of ARGUS 3D[J]. IEEE Aerospace and Electronic Systems Magazine, 2014, 29(3): 15–25. doi: 10.1109/MAES.2014.6805362. [104] FRÄNKEN D and ZEEB O. Advances in real-time tracking and data fusion using multiple passive radar sensors[C]. The 20th International Radar Symposium, Ulm, Germany, 2019: 1–10. [105] STEJSKAL V, KUSCHEL H, BRENNER T, et al. DETOUR trials: The mission and its results[C]. The 18th International Radar Symposium, Prague, Czech Republic, 2017: 1–14. doi: 10.23919/IRS.2017.8008191. [106] FRÄNKEN D and ZEEB O. Real-time creation of a target situation picture with the Hensoldt passive radar system[C]. The 21st International Conference on Information Fusion, Cambridge, UK, 2018: 500–506. doi: 10.23919/ICIF.2018.8455609. [107] OLIVADESE D, GIUSTI E, PETRI D, et al. Passive ISAR with DVB-T signals[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(8): 4508–4517. doi: 10.1109/TGRS.2012.2236339. [108] MARTORELLA M and GIUSTI E. Theoretical foundation of passive bistatic ISAR imaging[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(3): 1647–1659. doi: 10.1109/TAES.2014.130181. [109] PISCIOTTANO I, CRISTALLINI D, and PASTINA D. Maritime target imaging via simultaneous DVB-T and DVB-S passive ISAR[J]. IET Radar, Sonar & Navigation, 2019, 13(9): 1479–1487. doi: 10.1049/iet-rsn.2018.5622. [110] PISCIOTTANO I, SANTI F, PASTINA D, et al. DVB-S based passive polarimetric ISAR-methods and experimental validation[J]. IEEE Sensors Journal, doi: 10.1109/JSEN.2020.3037091. [111] GROMEK D, KULPA K, and SAMCZYŃSKI P. Experimental results of passive SAR imaging using DVB-T illuminators of opportunity[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(8): 1124–1128. doi: 10.1109/LGRS.2016.2571901. [112] FANG Yue, ATKINSON G, SAYIN A, et al. Improved passive SAR imaging with DVB-T transmissions[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(7): 5066–5076. doi: 10.1109/TGRS.2020.2972156. [113] GROMEK D, RADECKI K, DROZDOWICZ J, et al. Passive SAR imaging using DVB-T illumination for airborne applications[J]. IET Radar, Sonar & Navigation, 2019, 13(2): 213–221. doi: 10.1049/iet-rsn.2018.5123. [114] NITHIROCHANANONT U, ANTONIOU M, and CHERNIAKOV M. Passive multi-static SAR-experimental results[J]. IET Radar, Sonar & Navigation, 2019, 13(2): 222–228. doi: 10.1049/iet-rsn.2018.5226. [115] SANTI F, BUCCIARELLI M, PASTINA D, et al. Passive multistatic SAR with GNSS transmitters and using joint bi/multi-static CLEAN technique[C]. 2016 IEEE Radar Conference (RadarConf), Philadelphia, USA, 2016: 1–6. doi: 10.1109/RADAR.2016.7485109. [116] QIU Wei, GIUSTI E, BACCI A, et al. Compressive sensing-based algorithm for passive bistatic ISAR with DVB-T signals[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(3): 2166–2180. doi: 10.1109/TAES.2015.130761. [117] QU Lele, LIU Yu, AN Shimiao, et al. Multi-static airborne passive SAR imaging using cross-validation-based SOMP algorithm[J]. The Journal of Engineering, 2019, 2019(20): 7092–7095. doi: 10.1049/joe.2019.0587. [118] BOURNAKA G, BARUZZI A, HECKENBACH J, et al. Experimental validation of beamforming techniques for localization of moving target in passive radar[C]. 2015 IEEE Radar Conference (RadarConf), Arlington, USA, 2015: 1710–1713. doi: 10.1109/RADAR.2015.7131274. [119] EDRICH M, SCHROEDER A, and MEYER F. Design and performance evaluation of a mature FM/DAB/DVB-T multi-illuminator passive radar system[J].IET Radar, Sonar & Navigation, 2014, 8(2): 114–122. doi: 10.1049/iet-rsn.2013.0162. [120] EDRICH M, LUTZ S, and HOFFMANN F. Passive radar at Hensoldt: A review to the last decade[C]. The 20th International Radar Symposium (IRS), Ulm, Germany, 2019: 1–10. doi: 10.23919/IRS.2019.8768186. [121] DI LALLO A, FARINA A, FULCOLI R, et al. AULOS: Finmeccanica family of passive sensors[J]. IEEE Aerospace and Electronic Systems Magazine, 2016, 31(11): 24–29. doi: 10.1109/MAES.2017.160037.(请联系作者确认doi信息) [122] MARTELLI T, COLONE F, and CARDINALI R. Eco-friendly dual-band AULOS® passive radar for air and maritime surveillance applications[C]. 2018 IEEE International Conference on Environmental Engineering (EE), Milan, Italy, 2018: 1–6. doi: 10.1109/EE1.2018.8385267. [123] Patria. MUSCL, transportable and rugged passive radar[EB/OL]. https://www.patriagroup.com/products/passive-rf-sensors-product-family, 2020. [124] RZEWUSKI S, WIELGO M, KULPA K, et al. Multistatic passive radar based on WIFI-results of the experiment[C]. 2013 International Conference on Radar, Adelaide, Australia, 2013: 230–234. doi: 10.1109/RADAR.2013.6651990. [125] RIBÓ S, ARCO J C, OLIVERAS S, et al. Experimental results of an X-Band PARIS receiver using Digital Satellite TV opportunity signals scattered on the sea surface[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(9): 5704–5711. doi: 10.1109/TGRS.2013.2292007. [126] RAJA ABDULLAH R S A, SALAH A A, ISMAIL A, et al. Experimental investigation on target detection and tracking in passive radar using long-term evolution signal[J]. IET Radar, Sonar & Navigation, 2016, 10(3): 577–585. doi: 10.1049/iet-rsn.2015.0346. [127] COLONE F, MARTELLI T, BONGIOANNI C, et al. WiFi-based PCL for monitoring private airfields[J]. IEEE Aerospace and Electronic Systems Magazine, 2017, 32(2): 22–29. doi: 10.1109/MAES.2017.160022. [128] PASTINA D, SANTI F, PIERALICE F, et al. Maritime moving target long time integration for GNSS-based passive bistatic radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(6): 3060–3083. doi: 10.1109/TAES.2018.2840298. [129] SANTI F, PASTINA D, ANTONIOU M, et al. GNSS-based multistatic passive radar imaging of ship targets[C]. 2020 IEEE International Radar Conference (RADAR), Washington, USA, 2020: 601–606. doi: 10.1109/RADAR42522.2020.9114638. [130] 万显荣, 赵志欣, 柯亨玉, 等. 基于DRM数字调幅广播的高频外辐射源雷达实验研究[J]. 雷达学报, 2012, 1(1): 11–18. doi: 10.3724/SP.J.1300.2013.20001.WAN Xianrong, ZHAO Zhixin, KE Hengyu, et al. Experimental research of HF passive radar based on DRM digital AM broadcasting[J]. Journal of Radars, 2012, 1(1): 11–18. doi: 10.3724/SP.J.1300.2013.20001. [131] ZHAO Zhixin, WAN Xianrong, ZHANG Delei, et al. An experimental study of HF passive bistatic radar via hybrid sky-surface wave mode[J]. IEEE Transactions on Antennas and Propagation, 2013, 61(1): 415–424. doi: 10.1109/TAP.2012.2213062. [132] YI Jianxin, WAN Xianrong, CHENG Feng, et al. Computationally efficient RF interference suppression method with closed-form maximum likelihood estimator for HF surface wave over-the-horizon radars[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(4): 2361–2372. doi: 10.1109/TGRS.2012.2210903. [133] 谢锐, 万显荣, 洪丽娜, 等. 电离层行进式扰动对外辐射源天地波雷达系统的影响[J]. 电波科学学报, 2014, 29(6): 1098–1104, 1152. doi: 10.13443/j.cjors.2013103102.XIE Rui, WAN Xianrong, HONG Li’na, et al. Effects of the travelling ionospheric disturbance on sky-surface wave passive radar system[J]. Chinese Journal of Radio Science, 2014, 29(6): 1098–1104, 1152. doi: 10.13443/j.cjors.2013103102. [134] ZHAO Zhixin, WAN Xianrong, YI Jianxin, et al. Radio frequency interference mitigation in OFDM based passive bistatic radar[J]. AEU – International Journal of Electronics and Communications, 2016, 70(1): 70–76. doi: 10.1016/j.aeue.2015.10.004. [135] 谢锐, 万显荣, 赵志欣, 等. 外辐射源天地波雷达定位方法及精度分析[J]. 电波科学学报, 2014, 29(3): 442–449. doi: 10.13443/j.cjors.2013060902.XIE Rui, WAN Xianrong, ZHAO Zhixin, et al. Localization method and accuracy analysis in hybrid sky-surface wave passive radar[J]. Chinese Journal of Radio Science, 2014, 29(3): 442–449. doi: 10.13443/j.cjors.2013060902. [136] 张强, 万显荣, 傅䶮, 等. 基于CDR数字音频广播的外辐射源雷达信号模糊函数分析与处理[J]. 雷达学报, 2014, 3(6): 702–710. doi: 10.12000/JR14050.ZHANG Qiang, WAN Xianrong, FU Yan, et al. Ambiguity function analysis and processing for passive radar based on CDR digital audio broadcasting[J]. Journal of Radars, 2014, 3(6): 702–710. doi: 10.12000/JR14050. [137] FU Yan, WAN Xianrong, ZHANG Xun, et al. Side peak interference mitigation in FM-based passive radar via detection identification[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(2): 778–788. doi: 10.1109/TAES.2017.2665079. [138] YI Jianxin, WAN Xianrong, LEUNG H, et al. Joint placement of transmitters and receivers for distributed MIMO radars[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(1): 122–134. doi: 10.1109/TAES.2017.2649338. [139] LÜ Min, YI Jianxin, WAN Xianrong, et al. Cochannel interference in DTMB-Based passive radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 2019, 55(5): 2138–2149. doi: 10.1109/TAES.2018.2882959. [140] WEN Jinfang, YI Jianxin, and WAN Xianrong. Sparse representation for target parameter estimation in CDR-based passive radar[J]. IEEE Geoscience and Remote Sensing Letters, doi: 10.1109/LGRS.2020.2991743. [141] LIU Yuqi, WAN Xianrong, TANG Hui, et al. Digital television based passive bistatic radar system for drone detection[C]. 2017 IEEE Radar Conference (RadarConf), Seattle, USA, 2017: 1493–1497. doi: 10.1109/RADAR.2017.7944443. [142] SHU Kan, YI Jianxin, WAN Xianrong, et al. A hybrid tracking algorithm for multistatic passive radar[J]. IEEE Systems Journal, in press. doi: 10.1109/JSYST.2020.2994009 [143] LÜ Min, YI Jianxin, WAN Xianrong, et al. Target tracking in time-division-multifrequency-based passive radar[J]. IEEE Sensors Journal, 2020, 20(8): 4382–4394. doi: 10.1109/JSEN.2020.2964291. [144] FANG Gao, YI Jianxin, WAN Xianrong, et al. Experimental research of multistatic passive radar with a single antenna for drone detection[J]. IEEE Access, 2018, 6: 33542–33551. doi: 10.1109/ACCESS.2018.2844556. [145] SALAH A A, RAJA ABDULLAH R S A, ISMAIL A, et al. Experimental study of LTE signals as illuminators of opportunity for passive bistatic radar applications[J]. Electronics Letters, 2014, 50(7): 545–547. doi: 10.1049/el.2014.0237. [146] KLÖCK C, WINKLER V, and EDRICH M. LTE-signal processing for passive radar air traffic surveillance[C]. The 18th International Radar Symposium (IRS), Prague, Czech Republic, 2017: 1–9. doi: 10.23919/IRS.2017.8008105. [147] 王本静, 易建新, 万显荣, 等. LTE外辐射源雷达帧间模糊带分析与抑制[J]. 雷达学报, 2018, 7(4): 514–522. doi: 10.12000/JR18025.WANG Benjing, YI Jianxin, WAN Xianrong, et al. Inter-frame ambiguity analysis and suppression of LTE signal for passive radar[J]. Journal of Radars, 2018, 7(4): 514–522. doi: 10.12000/JR18025. [148] DAN Yangpeng, YI Jianxin, WAN Xianrong, et al. LTE-based passive radar for drone detection and its experimental results[J]. The Journal of Engineering, 2019, 2019(20): 6910–6913. doi: 10.1049/joe.2019.0583. [149] 万显荣, 刘同同, 易建新, 等. LTE外辐射源雷达系统设计及目标探测实验研究[J]. 雷达学报. 2020, 9(6): 967–973.WAN Xianrong, LIU Tongtong, YI Jianxin, et al. System design and target detection experiments for LTE-based passive radar[J]. Journal of Radar. 2020, 9(6): 967–973. [150] FRÄNKEN D and ZEEB O. Tracking and data fusion with the Hensoldt passive radar system[C]. The 22nd International Microwave and Radar Conference, Poznan, Poland, 2018: 404–407. doi: 10.23919/MIKON.2018.8405238. [151] CONTE E, D’ADDIO E, FARINA A, et al. Multistatic radar detection: Synthesis and comparison of optimum and suboptimum receivers[J]. IEE Proceedings F - Communications, Radar and Signal Processing, 1983, 130(6): 484–494. doi: 10.1049/ip-f-1:19830078. [152] ZHANG Xin, LI Hongbin, and HIMED B. Multistatic detection for passive radar with direct-path interference[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(2): 915–92. doi: 10.1109/TAES.2017.2667223. [153] MALANOWSKI M and KULPA K. Two methods for target localization in multistatic passive radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(1): 572–580. doi: 10.1109/TAES.2012.6129656. [154] NOROOZI A and SEBT M A. Target localization in multistatic passive radar using SVD approach for eliminating the nuisance parameters[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(4): 1660–1671. doi: 10.1109/TAES.2017.2669558. [155] NOROOZI A and SEBT M A. Algebraic solution for three-dimensional TDOA/AOA localisation in multiple-input- multiple-output passive radar[J]. IET Radar, Sonar & Navigation, 2018, 12(1): 21–29. doi: 10.1049/iet-rsn.2017.0117. [156] KLEIN M and MILLET N. Multireceiver passive radar tracking[J]. IEEE Aerospace and Electronic Systems Magazine, 2012, 27(10): 26–36. doi: 10.1109/MAES.2012.6373909. [157] BATTISTELLI G, CHISCI L, MORROCCHI S, et al. Robust multisensor multitarget tracker with application to passive multistatic radar tracking[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(4): 3450–3472. doi: 10.1109/TAES.2012.6324726. [158] KUSCHEL H, HECKENBACH J, and SCHELL J. Deployable multiband passive/active radar for air defense (DMPAR)[J]. IEEE Aerospace and Electronic Systems Magazine, 2013, 28(9): 37–45. doi: 10.1109/MAES.2013.6617097. [159] RADMARD M, KARBASI S M, and NAYEBI M N. Data fusion in MIMO DVB-T-Based passive coherent location[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(3): 1725–1737. doi: 10.1109/TAES.2013.6558015. [160] STINCO P, GRECO M S, GINI F, et al. Posterior Cramér–Rao lower bounds for passive bistatic radar tracking with uncertain target measurements[J]. Signal Processing, 2013, 93(12): 3528–3540. doi: 10.1016/j.sigpro.2013.02.021. [161] XIE Rui, WAN Xianrong, HONG Sheng, et al. Joint optimization of receiver placement and illuminator selection for a multiband passive radar network[J]. Sensors, 2017, 17(6): 1378. doi: 10.3390/s17061378. [162] DEL-REY-MAESTRE N, JARABO-AMORES M P, MATA-MOYA D, et al. Machine learning techniques for coherent CFAR detection based on statistical modeling of UHF passive ground clutter[J]. IEEE Journal of Selected Topics in Signal Processing, 2018, 12(1): 104–118. doi: 10.1109/JSTSP.2017.2780798. [163] CLEMENTE C, PARRY T, GALSTON G, et al. GNSS based passive bistatic radar for micro-Doppler based classification of helicopters: Experimental validation[C]. 2015 IEEE Radar Conference, Arlington, USA, 2015: 1104–1108. doi: 10.1109/RADAR.2015.7131159. [164] YONEL B, MASON E, and YAZICI B. Deep learning for passive synthetic aperture radar[J]. IEEE Journal of Selected Topics in Signal Processing, 2018, 12(1): 90–103. doi: 10.1109/JSTSP.2017.2784181. [165] MANNO-KOVACS A, GIUST E, BERIZZI F, et al. Image based robust target classification for passive ISAR[J]. IEEE Sensors Journal, 2019, 19(1): 268–276. doi: 10.1109/JSEN.2018.2876911. [166] 姚诗颖, 易建新, 万显荣, 等. 基于多层感知器的外辐射源雷达多帧联合检测[J/OL]. 电波科学学报. https://doi.org/10.13443/j.cjors.2020022301, 2020.YAO Shiying, YI Jianxin, WAN Xianrong, et al. Multi-frame joint detection for passive radar based on multi-layer perceptron[J/OL]. Chinese Journal of Radio Science. https://doi.org/10.13443/j.cjors.2020022301, 2020. -
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