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摘要: 极化合成孔径雷达(PolSAR)使用二维脉冲压缩技术获取高分辨力极化信息图像，目前已广泛应用在军事侦察、地形测绘、环境与自然灾害监视、海上舰船目标检测等领域。如何解决复杂海杂波的建模与参数估计、慢小目标检测、密集目标检测等问题仍然是当前PolSAR图像舰船目标检测的难点。该文归纳梳理了PolSAR图像舰船目标检测的4类主流方法：极化特征目标检测方法、慢速运动目标检测方法、舰船目标尾迹检测方法以及基于深度学习的目标检测方法等，同时给出了各类方法所存在的问题以及可能的解决方法，并预测了其未来研究重点和发展趋势。Abstract: Polarimetric Synthetic Aperture Radar (PolSAR) uses two-dimensional pulse compression to obtain high-resolution images containing polarimetric information. PolSAR has been widely used in military reconnaissance, topographic mapping, environmental and natural disaster monitoring, marine ship detection, and related fields. Addressing the problems associated with sea-clutter modelling and parameter estimation, slow and small target detection, dense target detection, as well as other issues, still remains a challenge in PolSAR ship detection. In this paper, four main classes for PolSAR ship detection are summarized: target polarimetric feature detection, slow and small target detection, ship wake detection, and deep learning detection. In addition, the possible solutions to existing problems in each class are given, and their future development trends are predicted, which can provide some valuable suggestions for interested researchers.
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Key words:
- Polarimetric SAR /
- Ship detection /
- Performance comparison /
- Development history /
- Future trend
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1. 引言
外辐射源雷达(又称为无源雷达)是一种利用第三方辐射源进行目标探测跟踪的双/多基地雷达系统。该体制雷达具有绿色环保、安全隐蔽、成本可控、易于组网、无需频谱分配等优势[1,2]。现阶段可利用的第三方照射源主要包括FM广播、数字音频广播信号(DAB)、数字电视广播(DVB-T, CMMB, DTMB)、全球微波互联接入信号(WiMAX)、WiFi信号等[3–10]。长期演进(Long Term Evolution, LTE)信号是由第3代合作伙伴计划(the 3rd Generation Partnership Project, 3GPP)组织制定的一种通用的无线通信信号，作为一种新型的外辐射源雷达机会照射源，受到了国内外的广泛关注。该信号作为第三方照射源具有独特的优势：(1)信号普及率高，易于组网，探测范围易扩展；(2)最大可支持20 MHz带宽，具有较高的距离分辨率；(3)超低空覆盖好，适用于地面与低空移动目标探测。
相对于广播或者电视信号的外辐射源雷达，LTE外辐射源雷达发展相对滞后。而随着4G时代的发展，LTE信号的覆盖范围逐步扩大，为研究该体制雷达提供了极为便利的条件，现阶段国内外已进行了一些探索性工作。马来西亚博特拉大学的Abdullah等人[11,12]利用频分双工长期演进(Frequency Division Duplexing Long Term Evolution, FDD-LTE)信号进行了地面移动目标探测以及目标分类识别研究。美国莱特州立大学的Evers等人[13–15]分析了具有扩展循环前缀(Cyclic Prefix, CP)的LTE信号的模糊函数，研究了典型副峰产生的机理，并介绍了利用FDD-LTE信号进行SAR成像的研究结果。武汉大学电波传播实验室研究了实测FDD-LTE信号的模糊函数特性，率先分析了帧间模糊带的形成机理，并提出了一种基于子载波系数归一化的帧间模糊带抑制方法[16,17]。中国科学院电子学研究所探讨了对于远距离目标探测有影响的相关副峰，并针对此类副峰给出了相应的抑制方法，最终得到图钉状的模糊函数[18]。据调研所及，国内有关利用LTE信号进行目标探测实验的研究尚无报道。
根据LTE外辐射源雷达探测需求，本文设计了一种高集成度、小型化的多通道外辐射源雷达系统。相对于传统外辐射源雷达系统，本系统具有以下优势：(1)通用性强，系统工作频率范围广且支持不同带宽，可涵盖所有频段的LTE信号；(2)集成度高，接收机系统采用高度集成的射频芯片实现，降低了系统的复杂程度；(3)传输速率快，采用万兆光纤传输方案，可满足不同带宽信号的传输要求；(4)成本较低，系统的高集成度、通用化优势也使得系统成本可控。系统的上述优势有利于实现LTE外辐射源雷达的组网探测，扩展雷达监测范围。
本文介绍了利用本系统开展国内首次基于LTE信号的地面及低空目标探测实验研究的进展，包括信号分析、雷达系统设计与实现、实验场景配置以及初步实验结果分析等。
2. LTE信号分析
2.1 信号帧结构
LTE信号根据双工方式可以分为频分双工(Frequency Division Duplexing, FDD)和时分双工(Time Division Duplexing, TDD)两种模式[19,20]，两者仅在物理层上略有区别。图1展示了这两种工作模式的框架结构，其中图1(a)为FDD-LTE帧结构，图1(b)为TDD-LTE帧结构。两者相同点在于系统以10 ms的无线帧为传输单位，每个无线帧由10个1 ms的子帧组成，每一个子帧又包含两个0.5 ms的时隙。每个时隙根据CP的不同包含不同数目的正交频分复用(Orthogonal Frequency Division Multiplexing, OFDM)符号，其中普通型CP包含7个OFDM符号，扩展型CP包含6个OFDM符号。两者区别在于FDD-LTE模式中系统在分离的两个对称频率信道上进行数据接收和传送，下行过程中整个无线帧都被应用于下行链路传输。TDD-LTE模式则是上下行使用相同的频段在不同时隙进行传输，一定数量的子帧用于下行链路传输，而另外一些子帧用作上行链路传输或者作为特殊子帧使用。相比之下，TDD-LTE含有多种传输模式且上下行信号的频段相同，导致多个终端的上行信号不可避免地干扰外辐射源雷达，使其难以准确划分下行信号时间窗，进而影响目标探测，FDD-LTE则可以避免上行信号的干扰。因此，本系统采用FDD-LTE信号进行目标探测。
2.2 模糊函数分析
模糊函数是研究外辐射源雷达波形特性的重要工具，它描述了外辐射源雷达系统采用的发射波形所具有的目标分辨率、测量精度、模糊度和杂波抑制能力。计算表达式如式(1)所示，其中s(t)为信号复包络，
τ 为距离时延，fd为多普勒频移。|A(τ,fd)|=|∫+∞−∞s(t)s∗(t−τ)e−j2πfdtdt| (1) 图2给出了实测FDD-LTE信号的模糊函数。从图2中可以看出，LTE信号模糊函数在零距离元和零多普勒频率处具有类似图钉形状的主峰。从时域和频域的梯度可以看出，其下降梯度较大，表明LTE信号具有良好的距离分辨率和速度分辨率。除主峰之外还存在多种有规律的模糊副峰，现有文献详细分析了LTE信号典型副峰的产生原因和位置[11–18]。依据副峰产生机理，副峰主要包括CP引起的副峰，控制区域信号(Control Channels, CCs)周期性引起的副峰，以及小区特定参考信号(Cell-specific Reference Signals, CRS)引起的副峰。
在不同的探测需求下，并非所有的副峰都会影响目标的探测。在城市环境下，一般基站的覆盖范围在1 km以内，常见目标(车辆、无人机、行人等)对应的多普勒频率在250 Hz以内。图2中CP引起的副峰相对于主峰时延为66.67 μs，对应双基距离达 20 km; CCs引起的副峰对应多普勒频率为1 kHz的整数倍，超出地面及低空典型运动目标的多普勒频率范围；CRS引起的副峰对应多普勒频率为2 kHz的整数倍，时延为11.11 μs的整数倍，同样超出目标多普勒频率和目标观测范围。综上所述，这些副峰对观测范围内的目标几乎无影响，本文中不考虑这些副峰的干扰。
3. LTE外辐射源雷达系统
3.1 系统工作原理
图3展示了LTE外辐射源雷达的工作原理，与其它外辐射源雷达系统类似，LTE外辐射源雷达在接收端同样分为参考通道与监测通道，分别用来接收直达波信号和目标的回波信号，通过相关处理可以得到目标的距离、速度以及方位等信息，从而实现对目标的检测和跟踪。
本文采用如图4所示的典型外辐射源雷达信号处理流程。首先对原始参考通道信号进行提纯，并利用提纯之后的参考信号与监测信号进行杂波抑制，以消除直达波以及多径干扰，从而突显目标回波[6,21]。之后参考信号和监测信号进行二维互相关处理，即匹配滤波，得到距离-多普勒谱(RD谱)。然后进行波束形成，最后通过恒虚警(Constant False Alarm Rate, CFAR)检测获得目标的双基距离和双基速度信息。
3.2 系统设计与实现
3.2.1 需求分析
LTE信号支持多种波段，且能够灵活配置不同的带宽，每个国家或地区可以根据自身的通讯环境需求以及频谱使用情况进行合理分配。表1展示了LTE信号的相关参数。从表中可以看出，LTE外辐射源雷达系统需满足以下要求：(1)工作频率范围广，满足700～3800 MHz的频段变化范围；(2)支持多种带宽分配，可根据具体需求配置不同带宽；(3)采样率可配置，实现对不同带宽信号的采样。
表 1 LTE信号基本参数Table 1. Basic parameters of the LTE signal参数 取值 频段(MHz) 700～3800 带宽(MHz) 1.4/3/5/10/15/20 采样率(MHz) 1.92/3.84/7.68/15.36/23.04/30.72 3.2.2 接收机方案设计
现阶段常用的雷达接收机系统，往往存在结构复杂、体积庞大、通用性较差、成本不可控等问题。本文选用高集成度的射频芯片AD9361完成接收机系统设计。该芯片内部集成零中频结构，不仅能大大降低对模数转换器(Analog-to-Digital Converter, ADC)性能的要求以及数字信号处理的复杂度，同时能减少模拟器件的数量，有利于实现系统小型化。芯片工作频率范围为70～6000 MHz，支持0.2～56.0 MHz的接收带宽，涵盖大部分的数字多媒体广播业务以及无线通信业务信号频段。仅需要简单控制就可实现对中心频率、带宽、滤波器参数以及增益等参数的配置，真正意义上做到系统的通用化。同时针对零中频结构本身的缺陷，其内部每个接收通道都具备直流失调校正和正交校正的功能，可以降低芯片本振泄露以及非正交带来的弊端。
为满足系统高速数据的实时传输需求，本文采用万兆光纤传输方案，实现接收机系统与上位机之间的数据交互。万兆光纤传输的主要优势在于其传输速率快，传输距离远。表2展示了系统的基本技术参数。
表 2 系统基本技术参数Table 2. Basic technical parameters of the system参数 取值 中心频率(MHz) 70～6000 带宽(MHz) 0.2～56.0 采样率 可配置 增益(dB) 0～76 传输速率(Gbps) 10 3.2.3 系统总体设计
图5展示了本系统的总体框图，主要由接收天线、多通道LTE外辐射源雷达接收机以及信号处理机组成。其中接收天线采用多元八木天线，包括参考天线与监测天线阵列。接收机直接与天线相连，将接收到的射频信号进行正交混频、采样、抽取滤波下变频为数字基带信号，然后由FPGA对数据进行打包，最后采用万兆光纤传输方案将数据传输至信号处理机。信号处理机一方面控制接收机实现增益、带宽以及中心频率等参数的配置，另一方面完成3.1节所述的信号处理流程，最终输出目标距离和速度信息。
4. 外场实验
4.1 实验场景
本文为验证LTE外辐射源雷达的目标探测性能，开展了合作目标探测实验。实验中选用中国电信FDD-LTE信号作为第三方照射源，其中心频率为1867.5 MHz，带宽为15 MHz。实验场景如图6所示。图6中左侧建筑物顶部的演进节点基站(evolved Node Bases, eNB)作为发射站，接收站位于建筑物前的道路上，参考天线指向eNB接收参考信号，监测阵列指向图中橘红色扇形所示的监测区域收集目标回波信号。目标移动范围为黄色线所示的地面以及低空区域。
实验主要针对地面移动目标以及低空移动目标进行探测。其中地面目标探测实验选用搭载了GPS设备的电动车作为合作目标，该电动车材质为高碳钢，轮圈尺寸为35.56 cm，最高速率为10 m/s。低空目标探测实验采用常见的消费级无人机大疆精灵4作为合作目标。该无人机旋翼数为4，每个旋翼叶片数为2，叶片长度为13.97 cm，轴距为35 cm，飞行速度可达20 m/s。实验中将电动车上GPS设备记录的数据以及无人机飞行记录中的GPS数据作为合作目标的真实信息，与系统检测得到的信息进行对比，验证系统的探测性能。
4.2 实验结果
图7展示了地面移动目标探测实验的结果。图7(a)为一场数据的距离多普勒谱，可观测到目标位于第15距离元，多普勒频率为70 Hz，信噪比为23 dB。为进一步确认其为实验所用之合作目标，本文将CFAR检测后的潜在目标信息与合作目标的GPS信息在同一RD谱上进行比较，如图7(b)所示。图中除合作目标之外，还有一些非合作目标(车辆、行人等)，但是通过与合作目标GPS信息对比可以看出检测结果与实际目标信息基本吻合，表明系统成功探测到地面移动目标。图7(c)与图7(d)展示了目标双基距离和双基速度随时间变化的情况。图中结果更直观地表明系统检测得到的信息与合作目标信息匹配度较好，能够真实地反应目标的移动规律。
图 7 LTE外辐射源雷达地面移动目标探测结果Figure 7. Experimental results of the ground moving target with the LTE-based passive radar图8展示了无人机目标探测实验的结果，图8(a)同样为目标位于第15距离元处的距离多普勒谱，无人机目标多普勒频率为122 Hz，信噪比为17 dB，其信噪比明显低于相同距离元处的地面移动目标。图8(b)为系统检测数据与无人机飞行记录的比对结果，虽然其信噪比普遍低于地面目标检测结果，但是检测的结果依然与无人机真实数据吻合，表明系统适用于无人机这类“低小慢”目标的探测。图8(c)和图8(d)展示的结果与图7对比可以看出，相比于地面目标，无人机检测效果略差，主要原因可能是无人机散射截面积(Radar Cross Section, RCS)明显小于电动车，目标回波强度较弱。另外由于无人机机动性更强，导致目标在转弯时双基速度变化更快，检测更加不连续。
图 8 LTE外辐射源雷达无人机探测结果Figure 8. Experimental results of the drone with the LTE-based passive radar系统检测得到的信息与合作目标真实信息比对的结果，证实了利用LTE信号实现地面及低空目标探测的可行性。
5. 结束语
本文首先介绍了LTE信号的物理层特性，在此基础上选用FDD-LTE信号作为第三方照射源进行研究。然后设计并实现了一种高集成度、小型化的通用外辐射源雷达系统，并利用此系统开展了国内首次基于LTE信号的地面及低空目标探测实验，为该探测技术的发展奠定了实验基础。后续将围绕更多不同目标开展实验，进行目标分类与识别研究，并进行组网探测研究，进一步挖掘该体制外辐射源雷达在目标监测领域的潜力。
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图 9 Moving target detection results in different polarimetric channels in PolSAR images[69]
图 10 Simulation results of polarimetric along-track interferometry moving target detection[76]
图 12 Ship wake detection results of low-rank and sparse decomposition combined with polarization[98]
表 1 PolSAR图像舰船目标检测方法适用场景和优缺点总结
Table 1. Applicable scenarios and pros & cons summary of PolSAR ship detection methods
检测分类 适用场景 具体检测方法 优点 缺点 目标极化特征检测 主要适用于舰船目标本体极化特征和杂波特征有一定差异情形下的检测 简单极化合成检测技术 早期PolSAR目标检测方法，简单易实现且效果优于单极化SAR图像目标检测方法 一定程度上利用了幅度或相位信息，对极化信息的利用不够充分 基于极化最优化的检测技术 1. 充分利用了各极化通道相关信息
2. 通过对PolSAR图像进行优化，达到最佳对比效果要根据实际情况选择不同的极化优化准则，同时滑窗的选择也对性能影响较大 基于散射机理的检测技术 具有较强的物理可解释性 对目标类型和海况状态的适应性需要提高 基于空间邻域的检测技术 融合空域与极化信息，大幅增强目标检测能力 后期数据处理需要降维以避免维数灾难 慢速运动目标检测 主要适用于目标杂波极化特征差异较小但有一定速度差异的情形，同时也能提取目标运动信息以获取实时海面态势 单通道慢动目标检测 利用交叉极化信息增强慢动目标的检测效果 静止目标模糊抑制和慢动目标检测难以同时实现 虚拟干涉慢动目标检测 解决了实际干涉数据来源缺乏的问题 子孔径分解的个数与重叠度的选择较为困难 多通道慢动目标检测 融合多平台和极化信息的优势，极大增强运动目标检测能力 复杂结构和成本限制工程应用 舰船目标尾迹检测 不是检测舰船目标本体，而是检测运动产生的尾迹，主要针对小目标和
隐身目标尾迹边缘线性特征检测 检测问题抽象成线形特征检测问题，方法相对简单 杂波背景下不同类型弱尾迹的检测困难 尾迹区域背景差异检测 尾迹区域和背景海面由于散射机理不同，在频谱特性、统计特征、极化特性等方面均存在较大差异，效果好 理论分析和方法实现更加复杂 基于深度学习的目标检测 不需要人工提取目标特征，在PolSAR图像智能解译中取得了巨大成功 基于卷积神经网络的检测方法 结构灵活、能够自动提取结构化特征，不仅能提取图像的低维特征，而且能提取图像的高维特征 1. 需要大量样本进行训练
2. 可解释性有待研究
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表 1 Applicable scenarios and pros & cons summary of PolSAR ship detection methods
Detection method Advantage Disadvantage Target polarimetric characteristic detection method (mainly applied to the detection of ship and clutter when the polarimetric characteri
stics are different)Simple polarimetric channel
synthesis technologyThe early PolSAR target dete
ction method is simple and easy to realize, and the effect is better than the single PolSAR image target detection methodTo some extent, the amplitude or phase information is used, but the polarimetric information is not fully utilized Polarimetric optimization
detection technology1. The information of each polari
metric channel is fully utilized
2. PolSAR images are optimized to achieve the best contrast effectDifferent optimization criteria should be selected according to the actual situation, and the selection of a sliding window also has a significant influence on the performance Detection technology based on the
scattering mechanismIt has strong physical interpre
tabilityAdaptability to target types and sea state needs to be improved Spatial neighborhood detection
methodThe fusion of spatial and polarime
tric information considerably enhances the target detection capabilityDimensions need to be reduced to avoid a dimensional disaster Slow-moving target detection method (mainly applied to the situation where the polarimetric characteristic difference between target and clutter is small but thesre is a certain velocity diffe
rence; simultaneously, it can also extract the target motion informa
tion to obtain the real-time sea surface situation)Single-channel PolSAR-GMTI Cross-polarimetric information is used to enhance the detection effect of slow-moving targets Suppression of stationary targets and detection of slow-moving targets are difficult to achieve simultaneously Virtual interferometric
slow-moving target detectionThe problem of the lack of actual interference data source is solved It is difficult to choose the number of subaperture decompo
sition and overlap degreeMultichannel PolSAR-GMTI Integrating the advantages of multi-platform and polarization information considerably enhan
ces the ability to move target detectionComplex structure and cost limit engineering applications Ship wake detection method (it is used not to detect the ship target body but to detect the wake gene
rated by the movement, mainly for small targets and stealth targets)Linear feature detection of
wake edgeThe detection problem is abstrac
ted into the linear feature detec
tion problem, and the method is relatively simpleIt is difficult to detect different types of weak wakes in the cluttered background Background difference detection
in the wake regionDue to the different scattering mechanisms of the wake region and background sea surface, there are considerable differences in spectrum characteristics, statisti
cal characteristics, polarimetric characteristics, and other aspects, and the effect is goodThe theoretical analysis and method implementation are more complex Target detection algorithm based on deep learning (it has achieved great success in intelligent PolSAR image interpretation without extracting target features man
ually)Detection method based on CNN Flexible structure can automa
tically extract structured features,
including both low-dimensional and high-dimensional features of images1. It needs a large number of samples to train with
2. Interpretability needs to be investigated
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