作者中心
专家审稿
责编办公
编辑办公
-
摘要: 相较于稀疏标量阵列和均匀多极化阵列，稀疏多极化阵列由于其可感知信号的极化状态、避免极化失配以及增加阵列自由度、减小互耦效应与降低硬件成本等优点，对其进行系统性研究具有重要的应用价值和理论指导意义。稀疏多极化阵列的设计较之于稀疏标量阵列的设计更加多样化，因其不仅与天线阵元位置有关，还与天线阵元极化种类和阵元指向等因素有关。该文首先对近年来该领域内相关研究进行归纳总结，从非均匀稀疏、均匀稀疏、混合均匀与非均匀稀疏3种稀疏方式出发，介绍和探究了主流稀疏多极化阵列结构优化方式，然后从基于深度学习的稀疏多极化阵列优化设计、稀疏多极化多输入多输出(MIMO)雷达、稀疏极化频率分集阵(PFDA)雷达和稀疏PFDA-MIMO雷达、稀疏多极化智能超表面以及稀疏多极化阵列在家居智能通信和工业物联网等复杂室内场景下的应用等方面对未来的发展方向进行了展望。Abstract: As compared with sparse scalar arrays and uniform diversely polarized arrays, sparse diversely polarized arrays show respectively advantages of possessing the ability of sensing the polarization state of source signals, avoiding polarization mismatch and increasing the degrees of freedom, reducing mutual coupling, decreasing hardware cost, etc. Therefore, the comprehensive research on sparse diversely polarized array is of great importance in both theory and realistic applications. The design architecture of sparse diversely polarized arrays, which are related not only to the location of the array elements, but also to the dipole/loop type, orientation, and polarization states of the antenna elements, are more diverse than those of sparse scalar arrays. This paper summarizes the relevant researches in this field in recent years, introduces and explores the mainstream sparse diversely polarized array structure optimization approaches from three aspects: non-uniformly sparse, uniformly sparse and mixed uniformly and non-uniformly sparse. Then the future development of sparse diversely polarized arrays is discussed in terms of deep learning-based optimization approach, sparse Multiple-Input Multiple-Output (MIMO) diversely polarized array, sparse Polarimetric Frequency Diverse Array (PFDA) radar and sparse PFDA-MIMO radar, sparse polarimetric reconfigurable intelligent surface, and the application of sparse diversely polarized array in complex indoor scenes, such as smart communications in house and Industrial Internet of Things.
-
1. 引言
机载雷达告警接收机(Radar Warning Receiver, RWR)是作战飞机用于雷达辐射源感知和威胁告警的电子对抗侦察系统[1,2]。它通过截获和分析照射到载机上的雷达信号，向飞行员提供雷达辐射源目标的方位、类型、威胁等级等信息，帮助飞行员掌握实时态势，提示飞行员采取恰当的电子对抗和战术规避等措施。它还可以引导干扰系统和反辐射导弹对高威胁目标实施干扰和打击，提高载机生存能力[3]。机载RWR针对的目标主要包括预警雷达、目指雷达、制导雷达、火控雷达、主动雷达导引头等[4]，其处理的主要雷达信号特征包括到达时间(Time of Arrival, TOA)、到达角(Direction of Arrival, DOA)、脉冲载频(Radio Frequency, RF)、脉冲宽度(Pulse Width, PW)、脉冲重复间隔(Pulse Repetition Interval, PRI)、脉冲幅度(Pulse Amplitude, PA)、信号调制样式等。近年来，经常随RWR一起出现的电子支援措施 (Electronic Support Measures, ESM)来源于美军电子战定义中的电子支援(Electronic Support, ES)，与中国“电子对抗”标准中的“电子对抗侦察”对应，威胁告警是其下的一种功能，雷达告警器(RWR)是针对特定系统功能出现的名词。三代机之前的RWR功能相对简单，主要是对照射到飞机上的特定频率雷达信号发出警告，并且指示威胁的大致方向，以上功能成为普遍认可的机载RWR基本功能。随着需求和能力的发展，RWR逐渐具有了传统意义上认为是ESM的功能，包括精确测向、无源定位能力、数据存储能力和更复杂的信号分析能力。这些能力可以增加告警威胁判断准确性，还可以延伸用于引导攻击，可以认为是告警能力的扩展；同时，实现这些功能的系统组成也是相互关联。因此，本文作者认为，在机载自卫电子对抗系统的划分上不应该区分出ESM功能和RWR功能，应统一称为RWR。
目前，机载RWR已经向数字化、模块化和认知化方向发展[5,6]。为了更好地总结机载RWR发展脉络，了解机载RWR信号处理相关技术，为未来机载RWR发展提供启示和建议。本文介绍了机载RWR的系统架构，详细分析了信号截获和参数测量、信号预处理、信号分选、信号识别、威胁评估5个机载RWR信号处理流程。从现代电磁环境的复杂性和雷达技术的发展出发，系统总结了目前机载RWR面临的挑战。从实际运用出发，指出对于机载RWR的单独综合射频孔径需求、基于系统最优的高性能指标需求、分级智能化需求、系统模块化需求、数据融合处理的需求和威胁评估简化的需求。
2. 机载RWR系统架构和信号处理流程发展现状
2.1 机载RWR系统架构发展历程
机载RWR的发展始于20世纪40年代，越南战争后成为飞机的标准设备。从接收机体制角度来看，机载RWR发展经历了两个阶段：模拟接收体制时代和数字体制时代。
2.1.1 模拟接收体制
早期的机载RWR接收机为模拟体制的宽带视频晶体接收机，基本的组成包括天线、接收机、信号处理部分和终端部分[7]，如图1所示。主要的特点是结构简单、截获概率高，但是低频率的分辨率低。模拟体制机载RWR系统采用顺序式处理系统架构，即雷达信号被天线截获后传输到接收机，接收机直接对雷达信号进行测量和处理，产生包含雷达信号重要参数的脉冲描述字。
模拟体制机载RWR的天线主要分为接收天线和专项天线。接收天线阵用来截获雷达信号，并与接收模块、分析处理单元测量和计算出雷达平台的方位、俯仰信息。为了保证信号截获的方向有效性和极化对准，机载RWR的接收天线大部分使用的是喇叭天线、平面螺旋天线和多波束天线。专项天线是面向一些特殊信号(红外、激光)而设置的告警天线[8]，这些信号往往是威胁级别很高的信号，能够直接反映对方的火控信息。但由于技术原因，接收天线无法截获这些信号，因此设立红外/激光专项天线。红外/激光信号被专项天线截获后，经过接收模块中的专门电路进行放大和变换并且产生数据码，而后产生的数据码被送给分析处理单元[9]。在后期的发展中，为了提高天线的测向精度，干涉仪系统被增加到机载RWR，其天线主要是用平面螺旋天线组合成的天线阵列。
早期RWR接收机带宽较窄，针对目标单一，不需要精确测频，所获取的信息基本可以满足告警要求。但是，随着接收机带宽越来越宽，目标雷达参数重合情况增多，利用载频信息分选和识别目标有了关键意义，因此对于测频的需求提高。为了提高测频精度，一种方法是在晶体视频接收机前端增加可调谐窄带带通滤波器，按照信号时序顺序接收，增强了对于频率的选择性，此类接收机称为可调谐射频接收机；另一种方法是在前端放置多个窄带带通滤波器，使用多个滤波器同时接收信号，此类接收机称为多信道接收机。由于晶体视频接收机信号处理能力较弱，接收机灵敏度低，逐步改为超外差接收机。早期典型的模拟体制机载RWR有AN/APR-25, AN/APR-26等。
随后，瞬时测频接收机广泛应用，其在瞬时带宽、频率测量精度、体积重量和成本等方面具有很大的优势。在实际运用中，常常将瞬时测频接收机和晶体视频接收机或者超外差式接收机配合使用。晶体视频接收机或者超外差式接收机测量脉冲幅度、脉冲起始时间和终止时间等参数，瞬时测频接收机测量每个脉冲的频率参数。瞬时测频接收机主要以数字化方式完成信号处理，此后，机载RWR逐渐由模拟体制向数字体制过渡。
早期的信号数据处理部分主要依靠硬件逻辑电路，输入信号与数据库信号进行匹配对比，完成信号的识别告警功能。随着对信号处理能力需求的增加，可编程微处理器逐渐应用在信号处理部分，可以在硬件不修改的前提下，通过软件修改实现对不同频率、不同脉冲重复频率等各种雷达的告警，典型的可编程机载RWR为AN/ALR-46。
2.1.2 数字接收体制
随着电子技术的发展和接收新体制雷达信号的需求提升，前端接收数字化采样技术被广泛应用，标志着机载RWR进入全数字化时代，世界上第1部全数字机载RWR为AN/ALR-69A(V)[10]。在此阶段，机载电子一体化成为发展趋势，机载RWR采用基于机载总线的系统架构，如图2所示。在射频截获部分采用机载射频孔径系统，信号的模拟处理模块前移，后端完全采用并行数字信号处理的方式实现，设备的各种部件通过高速的光纤总线/网络互连在一起。机载RWR设备内的单元可以分为两大类，一大类为数据采集和编码，主要功能为雷达信号的截获、测量和编码，包括前端接收单元、特殊波段接收单元、精确测向单元和基于紫外/红外的导弹逼近告警单元；另一大类为计算单元，主要是采用并行处理方式的分布式综合计算机阵列，主要功能为处理各种经过编码的射频信号和光电信号。整个机载RWR设备通过航空电子系统总线/网络和其他设备互连，以充分利用机上各种传感器资源如相控阵雷达、数据链、光电雷达等所得的信息，提高辐射源威胁识别和评估的可信度。
相比于顺序式机载RWR系统架构，基于机载总线的机载RWR系统架构主要的特点是信号数据依靠数据总线传输和信号处理实现了软件化。各传感器截获信号后，通过数模转换器完成数字化处理。数字化信号根据信号处理技术的不同分别提取不同的特征参数，特征参数数据依靠数据总线传输到综合处理器，综合处理器依靠嵌入式软件对信号进行处理并产生告警信息，告警信息和引导控制指令通过数据总线分别传输到前舱显示器和其他辐射单元。有的机载RWR系统还可以融合机载雷达、敌我识别器和数据链的信息辅助完成威胁告警。
目前，机载天线技术也得到了很大的发展，截获的频率范围扩展到2～40 GHz。相比于模拟体制的架构，数字化接收体制的机载RWR天线大多数采用4个宽带螺旋天线提供360°方位覆盖，4个数字化4象限接收机完成信号采集。前端接收机需要完成限幅预增大、覆盖频带划分、视频信号提取、扩展接收机动态范围4个功能，形成多频段、多通道的雷达视频信号和射频信号，便于后续部件处理[11]。较为先进的机载RWR设备实现了共型天线技术，例如F-22飞机中的AN/ALR-94告警器，它将30多部先进天线平滑地嵌入机翼和机身中，实现全方位、全频段的信号截获，并且具有先进的精确定位与识别系统(Precision Location and Identification, PLAID)，可以采用单阵元测多普勒频率、双阵元构成干涉仪测相位差变化率及时延测向对地面固定辐射源进行粗定位及精定位。
2.2 机载RWR信号处理流程
机载RWR系统的总体信号处理流程如图3所示。主要分为5个过程，分别是信号截获和参数测量、信号预处理、信号分选、信号识别和威胁评估，最后将告警信息告知飞行员并引导干扰设备[12]。
2.2.1 雷达信号截获和参数测量
电磁环境中所有类型的交叠信号被天线截获后进入前端接收机，前端接收机通过瞬时测频和瞬时测向设备完成信号RF, TOA, PW, PA, DOA等基本参数的测量。目前，部分先进的接收机还具备对脉内调制类型和信号指纹特征等特殊参数提取的功能。前端接收机完成参数测量后，按照到达时间将接收的信号形成雷达脉冲特征参数数据列表，数据列表记录了每一段雷达信号的详细特征，而后将数据列表传输到后端处理器。
2.2.2 信号预处理
后端处理器接收到的初始数据列表包含了天线可截获范围内所有外部电磁环境的电磁信号，具有脉冲交叠严重和脉冲数据密度大的特点。对于初始数据列表，后端处理器直接处理难度较大，因此需要对初始数据进行预处理。信号预处理环节主要进行信号稀释和已知/未知信号快速匹配关联[13]，如图4所示。信号稀释通常根据雷达信号工作频段进行信号频域划分和根据雷达信号到达角进行空域划分，同时将大量民用通信信号、二次雷达识别信号和己方辐射源等不感兴趣信号删除。
经过脉冲稀释的脉冲流信号再与已知信号数据库进行匹配对比，从而分离出已知信号子脉冲流和未知信号子脉冲流并且存储到缓存器中，以便后续信号分选的读取[14]。在预处理中，通常选择TOA, RF, PW作为对比的特征参数。信号预处理的过程也可以看出对于前端接收的脉冲数据流分成无用信号、已知信号和未知信号的过程，达到减少后续信号处理的负担。
在系统中，实现数据预处理算法的软硬件电路和系统称为预处理机[15]。传统的预处理机通常由锁存器、比较器、存储器和逻辑电路组成，随着数字技术的发展，采用并行DSP阵列构成预处理器和采用FPGA电路构成的预处理器被广泛应用。
2.2.3 信号分选
信号分选是对感兴趣的雷达信号进一步精确分类，将雷达信号按照不同类型不同平台进行归类，最终将交叠的雷达信号分成一个个同类型同平台的信号列表。早期的分选主要采用的信号参数为PRI。由于早期的雷达信号在同一相参处理周期内脉冲的PRI保持不变，因此可以通过对比PRI值，将相同数值的脉冲序列归为一类。典型的方法包括直方图算法[16,17]、PRI变换法[18,19]。随着雷达技术的进步，PRI的调制方式也更加多样，从单一重复调制逐渐变为滑变、抖动、参差等调制样式。调制样式的多变导致基于单参数PRI的分选效果显著下降，一些学者考虑将单独依靠PRI参数特征扩展成多个参数特征进行分选。多参数分选主要分为关联比较分选[20]和多参数联合聚类分选。多参数关联比较法又称小盒分选，主要是根据DOA, PW, RF等参数对威胁数据库记录的辐射源数据进行关联。多参数联合聚类分选法是利用聚类算法对雷达信号进行无监督分组，主要包括基于划分聚类的分选方法[21]、基于层次聚类的分选方法[22]、基于网格聚类的分选方法[23]、基于密度聚类的分选方法[24,25]、基于模糊聚类的分选方法[26,27]等。随着机器学习技术的快速发展，越来越多的学者也开始探究其相关技术在雷达信号分选中的应用。基于机器学习算法的雷达信号分选主要将大量的带有标签的辐射源数据列表作为训练集输入到智能网络中，智能网络通过对已知数据的估计或近似建立适应性网络。机器学习算法强大的学习能力和数据处理能力，能够同时完成信号分选和识别功能，因此基于机器学习算法的雷达信号分选即识别，两者的应用算法具有相似性。为了减少赘述，相关算法在2.4节介绍。
随着新体制雷达工作模式不断拓展，信号样式和调制类型越来越复杂。不同雷达的基本特征参数交叠严重，难以区分，因此学者开始研究提取雷达信号脉内瞬时特征[28]、统计特征向量[29]、高阶频谱[30]、多重分形谱[31]、双谱对角切片[32]等其他特征。这些研究从不同维度挖掘信号脉内信息，拓展了雷达信号特征体系，为信号分选提供丰富的特征输入。
相比情报侦察(Electronic Intelligence, ELINT)系统，机载RWR信号分选要求很高的实时性、准确性、自动性。因此在保证高准确率的同时提升算法的运算速度以及智能性是今后研究的重点。
2.2.4 信号识别
经过前期的分选处理，交叠的雷达脉冲信号被分离成一个个单部雷达辐射源的参数特征。目标识别的环节是根据辐射源的参数特征判断平台类型，传统机载RWR的目标识别采用预识别、主识别以及相关识别3级处理结构，基本的目标处理流程如图5所示。
早期的雷达辐射源识别方法有参数匹配法、专家系统法等。参数匹配法又称模板匹配法，是利用信号特征参数与已知的威胁数据库进行匹配，识别雷达辐射源的属性信息[33]。该方法具有识别速度快、易于实现等优点，但过于依赖先验知识，缺乏推理能力。专家系统法根据专家提供的雷达属性知识，构建雷达信号识别的推理规则，对雷达辐射源数据进行推理和识别，具有一定的学习和推理能力[34]，但实现依赖于海量的雷达信号参数实例及雷达属性知识。该方法的识别效率较低，识别速度较慢。
近年来，机器学习算法在识别的优势促使越来越多的研究人员将最新的机器学习成果应用到雷达辐射源识别的研究中[35]。目前广泛应用到雷达辐射源识别的机器算法有神经网络(Neural Network)[36]、支持向量机(Support Vector Machine, SVM)[29]。随着深度学习算法的发展，卷积神经网络(Convolutional Neural Networks, CNN)[37,38]、循环神经网络(Recurrent Neural Network, RNN)[39]、域对抗神经网络[40]、深度置信网络(Deep Belief Network, DBN)[41]、栈式降噪自编码器(stack Denoise Auto-Encoder, sDAE)[42]、长短期记忆网络(Long Short Term Memory, LSTM)[43]等算法在雷达识别领域得到广泛的研究。此外，极限学习机(Extreme Learning Machine, ELM)[44]，集成学习(weighted-xgboost)[45]、AdaBoost[46]、随机森林[47]、强化学习[48,49]等算法也应用在雷达识别领域。
目前的机载RWR的雷达信号识别主要任务为平台类型识别，并不能准确地判别雷达工作模式。在日益激烈的电子对抗中，双方的攻守之势从以前仅依靠空中态势转变为同时依靠机载电子设备发射的电磁信号信息和空中态势信息。能够准确掌握对方雷达的工作模式成为自身威胁评估的重要前提。目前，雷达工作模式识别主要有基于模型和基于参数的两大类识别方法。基于模型的工作模式识别是通过对雷达系统进行建模实现雷达工作模式的识别和预测，建立模型的方法主要有隐马尔可夫模型[50]、句法模型[51]、预测状态表示模型[52]、生物工程模型[53]等。如果具有完备的先验知识，基于模型的工作模式识别能够将雷达工作模式完备地表现出来，甚至可以预测雷达工作模式。基于参数的雷达工作模式识别主要通过提取雷达信号特征参数，利用深度学习进行。
在电磁环境日益复杂和雷达技术不断发展的情况下，对雷达识别技术的要求不断增加。对机载RWR的雷达识别的准确性和智能性方面的要求不断增加，同时也增加了对已知信号的快速识别和未知信号准确推理的需求。
2.2.5 威胁评估
机载RWR最主要的目的就是进行威胁评估，实现对全域的威胁感知，这是区别于ELINT最大的特征。前期信号预处理、信号分选和信号识别等环节都是为这一最终目的提供支持。机载RWR根据态势信息和辐射源信息计算威胁程度，最终的结果传输到座舱的屏显画面。同时，根据设定的程序引导有源干扰或者无源干扰，使其按照设定的干扰样式和干扰(投放)方案进行自主对抗。目前，由于雷达工作模式识别不确定性高，并且对于飞行员来说主要关心的是雷达的工作状态(跟踪状态、制导状态)、平台类型和敌方导弹杀伤边界，因此，在屏显画面上只对跟踪或者制导信号的平台进行特殊标记。当机载RWR检测到有来袭导弹时，不仅在屏幕显示器上进行特殊标记，还会以语音的形式进行提醒。
学者对于威胁评估算法开展了广泛研究，主要的方法包括多属性决策理论[54]、直觉模糊集(Intuitionistic Fuzzy Sets, IFS)[55]、贝叶斯网络[56](Bayesian Network, BN)、多目标排序[57]、机器学习[58,59]、云模型[60]、雷达图法[61]等方法。多属性决策理论应用得较为广泛，灰主成分[62]、线性回归分析[63]、动态变权[64]、粗糙集[65,66]等方法被用于改进多属性决策的性能。随着电磁环境中电磁脉冲密度急剧增加以及新体制雷达的广泛应用，对信号分选识别和辐射源测向带来了极大挑战，单单依靠信号信息无法快速准确进行威胁评估。针对以上问题，作者所在的团队[67-70]提出了将自身雷达探测信息和告警器截获的辐射源信息相融合的威胁评估思想。
3. 机载RWR发展趋势
3.1 机载RWR面临的挑战
现今，作战飞机面临的辐射源种类和数量急剧增加，雷达信号在空、时、频域交叠日益严重，这些对机载RWR带来极大的处理压力和处理难度。同时作为对抗的主要目标——雷达，其技术高速发展：接收和发射体制实现全方面数字化处理、低截获技术和相控阵广泛应用、波束捷变能力大幅度增强、信号参数变化能力增强[71]、雷达软件化趋势明显[72,73]。电磁环境的变化和雷达技术的发展都对机载RWR提出了新的挑战。
(1) 日益复杂的电磁环境对雷达信号分选识别的准确度提出了更高要求。新体制雷达参数的复杂多变造成了分选时的“增批”现象严重，有时将单个辐射源判别成多个辐射源，给飞行员的判断造成了极大的困扰。同时，雷达种类的增多和雷达工作频率区间的重复使用，造成了雷达识别经常混淆的问题，尤其对于机载雷达，工作波段集中在X波段区间附近，PW和PRI等工作参数也存在交叠现象。
(2) 日益复杂的电磁环境对数据接收和处理能力提出了更高要求。电磁环境中各种辐射源类型的增多和数量的增加，使电磁环境密度急剧增加。有相关研究表明，现代电磁环境中的脉冲密度超过100万脉冲/s，甚至可达到500万脉冲/s。前端接收模块在面对如此大的电磁环境密度时，经常会出现接收机饱和的现象，堵塞了接收机的截获通道，导致漏警情况的发生。后端处理模块面对如此大的电磁环境密度时处理能力不足，导致部分雷达脉冲由于处理不及时而被抛弃的情况发生。
(3) 先进体制雷达技术的发展对雷达工作状态的有效判断提出了更高要求。对于早期的机械扫描雷达，机载RWR根据幅度、波束停留时间、频率等信息可以精确判断对方雷达是否进入跟踪或者制导状态。随着采用相位扫描体制雷达的广泛应用，雷达的搜索状态和跟踪状态的参数界限逐渐模糊，使机载RWR不能及时准确地判断对方雷达是否进入跟踪或者制导状态，造成了威胁评估极大的不确定性。
(4) 先进体制雷达技术的发展对未知目标的识别和未知威胁的推理提出了更高要求。由于国家安全的需要，各国严格把控各国的辐射源参数信息，辐射源参数的先验情报较少。而且，新体制雷达大部分实现软件化，辐射波形和辐射参数可以通过快速编程实现变化，总会出现威胁数据库中没有的信号特征。现有的雷达识别技术依赖于先验威胁数据库，无法识别未知目标。威胁评估技术主要根据威胁目标的时域、空域和频域等现有信息进行评估，无法对未知的威胁进行推理。
3.2 机载RWR需求分析
随着信息技术的发展，现代战争的作战样式发生了改变，各种新型雷达也广泛应用，除了数字化和一体化的必然趋势，也给机载RWR带来了新的需求：
(1) 单独射频孔径的需求。为了提升飞机的隐身性能，射频综合孔径一体化成为发展趋势[74]。但是，在使用中由于受到飞机自身资源的限制，接收天线需要和雷达、通信等设备分频分时使用，容易产生漏警现象。机载RWR作为与载机生存直接相关的特殊机载电子设备，在飞机进入到敌导弹发射射程内后，需要全时、全方位和重点频段接收信号。因此，本文认为需要提供机载RWR天线单独的射频孔径，尤其在关系飞机自身安全的重点频段，其他频段可以与其他系统采用综合孔径的方式。
(2) 基于系统最优的高性能指标需求。灵敏度、瞬时带宽覆盖范围、动态范围、频率分辨率和参数的精确度是机载RWR的重要性能指标。其指标的好坏直接影响机载RWR系统的性能。高的灵敏度可以使截获距离更远，但是同样也会对信号处理带来压力。宽的瞬时频率覆盖可以截获范围更广的各种频率的雷达信号，但是也会带来接收数据量增大的问题。因此，要根据机载RWR设计的目的和信号处理能力合理地规划机载RWR各项指标。使系统达到最优的各项性能指标是机载RWR的基础需求。
(3) 分级智能化的需求。随着认知电子战概念的突出，电子战设备更加注重智能化[75]。但是，智能网络的更新训练过程较慢，对于电磁环境的变化需要一定时间的适应，满足不了实时性的要求。文献[76]提出将机载RWR分为前级告警和后级告警两大模块，前级模块针对已知信号和简单信号进行快速告警，主要采用传统信号识别算法实现快速告警，后级模块针对前级未成功告警的数据进行准确告警，采用各种深度学习算法并结合其他数据源信息进行综合推理，完成精确告警，还可以对行动意图和未知威胁进行推理与告警，实现超前告警。前后级的信息可以相互使用，前级告警结果可以作为先验知识引导后级处理，加快后级处理的收敛速度，后级处理结果可以作为已知信息更新前级威胁数据库。
(4) 系统模块化的需求。对于电子设备而言，有着著名的“摩尔定律”，往往一种新型机载RWR研制成功时，其内部部分元器件已经严重落后，造成了设备的重复研制[77]。为了降低设备的研发成本、简化设备的后期技术维护，节约经费，加快设备的更新换代速度，需要实现机载RWR的模块化设计。模块化的设计就是在标准化的架构下，通过各个功能模块的组建构成弹性的机载RWR系统，每个功能模块可以快速拆解更换和单独升级。模块化的机载RWR系统根据任务和对象的不同，可以快速构建各种功能和性能指标的机载RWR系统。
(5) 数据融合处理的需求。着眼于体系化作战的需求，机载航电系统更加趋于综合化和一体化，机载RWR将成为综合航电系统的一部分[78]。综合航电一体化的主要特点是各电子系统只进行数字化处理和信号处理，数据的处理部分由中央综合处理器完成。各机载电子设备的数据可以共享和融合处理。机载RWR数据可以引导雷达和干扰设备的辐射，实现精辐射源的精确控制，甚至可以直接向武器系统提供目标位置信息。同时，机载RWR可以融合雷达数据弥补机载RWR测距不准的缺陷，提升定位速度和定位精度，也可以利用敌我识别系统和数据链的目标属性信息，实现对目标精准的威胁评估。
(6) 威胁评估简化的需求。目前的威胁评估主要通过处理分选识别后的平台信息、工作状态信息和信号脉冲描述字。雷达参数的复杂多变使依靠参数信息的识别准确度下降严重。对此，减少对于参数信息依赖的威胁评估是目前的迫切需求。发展以雷达行为特征和载机行为特征作为依据的评估技术，增强机载RWR对于雷达目标工作状态转换的敏感性和对雷达信号特征的分析能力。在威胁评估上从参数评估转化为行为评估，简化威胁评估的过程和方法。
4. 结语
本文从接收机体制角度来划分，将机载RWR分为模拟接收体制和数字接收体制两个阶段，分析了每个阶段的硬件技术和特点。同时，本文详细梳理了机载RWR的信号截获和参数测量、信号预处理、信号分选、信号识别和威胁评估5个信号处理流程，对每个处理流程的主要功能进行介绍，同时在信号分选、信号识别和威胁评估部分系统阐述了相关技术与算法的发展。最后，系统总结了现代电磁环境的复杂性和雷达技术在机载RWR的雷达信号分选识别能力、数据接收和处理能力、雷达工作状态的有效判断能力、未知目标的识别和未知威胁的推理能力的挑战。同时指出在数字化和一体化必然趋势下对于机载RWR的单独综合射频孔径需求、基于系统最优的高性能指标需求、分级智能化需求、系统模块化需求、数据融合处理的需求和威胁评估简化的需求，为机载RWR的发展提供启示和建议。
-
图 1 由6个NA偶极子阵元(轴向平行于y 轴)及10个偶极子ANA阵元(轴向平行于z 轴)组成的稀疏多极化阵列结构示意图
Figure 1. Sparse diversely polarized array composed of 6 NA dipoles (axial directions parallel to the y-axis) and 10 ANA dipoles (axial directions parallel to the z-axis)
图 2 由两层嵌套EMVS阵列组成的稀疏多极化阵列结构示意图
Figure 2. Sparse diversely polarized array composed of two-layer nested EMVS subarrays
图 3 由两层嵌套稀疏拉伸EMVS阵列组成的稀疏多极化阵列结构示意图
Figure 3. Sparse diversely polarized array composed of two-layer nested sparse stretched EMVS subarrays
图 5 由EMVS构成的矩形稀疏多极化阵列结构示意图
Figure 5. Rectangular sparse diversely polarized array constructed by EMVS
图 6 由3种指向的偶极子组成的稀疏L型多极化阵列结构示意图
Figure 6. Sparse L-shaped diversely polarized array composed of dipoles with three directions
图 8 空域分置交叉偶极子稀疏矩形多极化阵列结构示意图
Figure 8. Sparse rectangular diversely polarized array composed of spatially distributed cross-dipoles
图 9 轴向平移多线性稀疏多极化阵列结构示意图
Figure 9. Axial translation multilinear sparse diversely polarized array
图 10 以非均匀稀疏阵为子阵均匀稀疏摆放的多极化阵列结构示意图
Figure 10. Diversely polarized array composed of uniformly distributed non-uniform sparse subarrays
图 11 以均匀稀疏阵为子阵非均匀稀疏摆放的多极化阵列示意图
Figure 11. Diversely polarized array composed of non-uniformly distributed uniform sparse subarrays
图 12 图11所对应的均匀配置多极化阵列示意图
Figure 12. Uniformly distributed diversely polarized array corresponding to Fig. 11
图 13 从传统MIMO雷达到稀疏多极化MIMO雷达的发展脉络
Figure 13. Evolution from traditional MIMO radar to sparse diversely polarized MIMO radar
图 14 稀疏多极化RIS示意图(一种多极化配置方式)
Figure 14. Sparse polarimetric RIS (one diversely polarized configuration)
图 15 稀疏多极化RIS示意图(另一种多极化配置方式)
Figure 15. Sparse polarimetric RIS (another diversely polarized configuration)
表 1 稀疏多极化阵列设计研究的技术背景、理论基础、设计方法种类和设置及约束方式
Table 1. Technical background, theoretical basis, categorization of the configuration design, and setting/constraint approaches for sparse diversely polarized array
技术背景 理论基础 设计种类 设置及约束方式 1 均匀阵列阵元间互耦效应强2 均匀阵列自由度小3 均匀阵列硬件成本高4 标量稀疏阵列无法感知信号极化信息 1 互质阵和嵌套阵等稀疏阵列设计准则2 多极化阵列的矢量叉积性质 1 非均匀稀疏多极化阵列设计2 均匀稀疏多极化阵列设计3 混合均匀与非均匀稀疏多极化阵列设计 1 以互质阵和嵌套阵等稀疏阵为基本稀布约束方式，由不同极化方式的天线联合构建2 等距地稀布不同极化方式的天线3 融合上述两种设置方式分别约束阵列的均匀部分与非均匀部分 
下载: 导出CSV
表 2 标量与多极化MIMO雷达优缺点总结
Table 2. Summary of advantages and disadvantages of scalar and diversely polarized MIMO radars
MIMO雷达类型 主要优势 主要缺点 标量 均匀[54-62] 生成虚拟阵列，增加阵列孔径和自由度[76] 1 阵元间互耦效应降低估计精度；2 系统成本较高 1 极化失配
造成估计
精度损失；2 无法感知
信号极化
信息稀疏[63,69] 最小冗
余[63,64]1 进一步提升阵列孔径和自由度；2 减小阵元间互耦效应，提高角
度估计精度；3 降低系统成本1 阵元位置求解较复杂；2 缺少阵列孔径的一般表达式 嵌套[65,66] 存在间距较密阵元导致的互耦效应 互质[67,68] 差合阵存在孔洞 多极化 均匀[20,70-72] 减小极化失配，
提升角度估计
精度，增强极
化信息处理
能力生成虚拟阵列，增加阵列
孔径和自由度1 阵元间互耦效应降低估计精度；2 系统成本较高 稀疏[73-75] 1 进一步提升阵列孔径和自由度；2 减小阵元间互耦效应，提高角
度估计精度；3 降低系统成本阵列的多极化合理配置仍是一大难点
下载: 导出CSV
表 3 FDA雷达、FDA-MIMO雷达和PFDA-MIMO雷达优点总结
Table 3. Summary of potential advantages of FDA radar, FDA-MIMO radar and PFDA-MIMO radar
下载: 导出CSV
-
[1] HE Jin, WANG Yijing, SHU Ting, et al. Polarization, angle, and delay estimation for tri-polarized systems in multipath environments[J]. IEEE Transactions on Wireless Communications, 2022, 21(8): 5828–5841. doi: 10.1109/TWC.2022.3143834 [2] ZHU Dalin, CHOI J, and HEATH R W. Two-dimensional AoD and AoA acquisition for wideband millimeter-wave systems with dual-polarized MIMO[J]. IEEE Transactions on Wireless Communications, 2017, 16(12): 7890–7905. doi: 10.1109/TWC.2017.2754369 [3] YUE Yaxing, XU Yougen, LIU Zhiwen, et al. Parameter estimation of coexisted circular and strictly noncircular sources using diversely polarized antennas[J]. IEEE Communications Letters, 2018, 22(9): 1822–1825. doi: 10.1109/LCOMM.2018.2849402 [4] WANG Zhanling, YIN Jiapeng, PANG Chen, et al. An adaptive direction-dependent polarization state configuration method for high isolation in polarimetric phased array radar[J]. IEEE Transactions on Antennas and Propagation, 2021, 69(6): 3257–3272. doi: 10.1109/TAP.2020.3037704 [5] FRIEDLANDER B. Polarization sensitivity of antenna arrays[J]. IEEE Transactions on Signal Processing, 2019, 67(1): 234–244. doi: 10.1109/TSP.2018.2880708 [6] SHEN Shanpu, ZHANG Yujie, CHIU C Y, et al. A triple-band high-gain multibeam ambient RF energy harvesting system utilizing hybrid combining[J]. IEEE Transactions on Industrial Electronics, 2020, 67(11): 9215–9226. doi: 10.1109/tie.2019.2952819 [7] 庄钊文, 徐振海, 肖顺平, 等. 极化敏感阵列信号处理[M]. 北京: 国防工业出版社, 2005.ZHUANG Zhaowen, XU Zhenhai, XIAO Shunping, et al. Signal Processing of Polarization Sensitive Array[M]. Beijing: National Defense Industry Press, 2005. [8] ZHAO Kang, LIU Zhiwen, SHI Shuli, et al. Polarimetric clutter nulling space-time adaptive processing[C]. The 2020 4th International Conference on Digital Signal Processing, Chengdu, China, 2020: 331–335. [9] KHAN S and WONG K T. A six-component vector sensor comprising electrically long dipoles and large loops - To simultaneously estimate incident sources’ directions-of-arrival and polarizations[J]. IEEE Transactions on Antennas and Propagation, 2020, 68(8): 6355–6363. doi: 10.1109/TAP.2020.2988980 [10] PAL P and VAIDYANATHAN P P. Nested arrays: A novel approach to array processing with enhanced degrees of freedom[J]. IEEE Transactions on Signal Processing, 2010, 58(8): 4167–4181. doi: 10.1109/TSP.2010.2049264 [11] LIU Jianyan, ZHANG Yanmei, LU Yilong, et al. Augmented nested arrays with enhanced DOF and reduced mutual coupling[J]. IEEE Transactions on Signal Processing, 2017, 65(21): 5549–5563. doi: 10.1109/TSP.2017.2736493 [12] ZHOU Chengwei, GU Yujie, FAN Xing, et al. Direction-of-arrival estimation for coprime array via virtual array interpolation[J]. IEEE Transactions on Signal Processing, 2018, 66(22): 5956–5971. doi: 10.1109/TSP.2018.2872012 [13] ZHENG Wang, ZHANG Xiaofei, WANG Yunfei, et al. Padded coprime arrays for improved DOA estimation: Exploiting hole representation and filling strategies[J]. IEEE Transactions on Signal Processing, 2020, 68: 4597–4611. doi: 10.1109/TSP.2020.3013389 [14] ZHENG Zhi, WANG Wenqin, KONG Yangyang, et al. MISC array: A new sparse array design achieving increased degrees of freedom and reduced mutual coupling effect[J]. IEEE Transactions on Signal Processing, 2019, 67(7): 1728–1741. doi: 10.1109/TSP.2019.2897954 [15] SHEN Qing, LIU Wei, CUI Wei, et al. Simplified and enhanced multiple level nested arrays exploiting high-order difference co-arrays[J]. IEEE Transactions on Signal Processing, 2019, 67(13): 3502–3515. doi: 10.1109/TSP.2019.2914887 [16] FAN Xing, ZHOU Chengwei, GU Yujie, et al. Toeplitz matrix reconstruction of interpolated coprime virtual array for DOA estimation[C]. 2017 IEEE 85th Vehicular Technology Conference (VTC Spring), Sydney, NSW, Australia, 2017: 1–5. [17] ZHOU Chengwei, SHI Zhiguo, GU Yujie, et al. Coarray interpolation-based coprime array DOA estimation via covariance matrix reconstruction[C]. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing, Calgary, AB, Canada, 2018: 3479–3483. [18] YUE Yaxing, ZHANG Zongyu, ZHOU Chengwei, et al. Closed-form two-dimensional DOA and polarization joint estimation using parallel non-collocated sparse COLD arrays[C]. 2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop, Trondheim, Norway, 2022: 16–20. [19] TANG Mang, SHU Ting, HE Jin, et al. Direction-finding and polarization estimation with spread orthogonal loop and dipole arrays[J]. Circuits, System, and Signal Processing, 2021, 40(12): 6401–6415. doi: 10.1007/s00034-021-01776-9 [20] YUE Yaxing, XU Yougen, and LIU Zhiwen. Manifold separation and polarimetric element space based parameter estimation for polarimetric monostatic MIMO radar[C]. CIE Radar Conference, Haikou, China, 2021: 573–577. [21] SI Weijian, ZENG Fuhong, QU Zhiyu, et al. Two-dimensional DOA estimation via a novel sparse array consisting of coprime and nested subarrays[J]. IEEE Communications Letters, 2020, 24(6): 1266–1270. doi: 10.1109/LCOMM.2020.2979066 [22] YANG Yunlong, HOU Yuguan, MAO Xingpeng, et al. Stokes parameters and DOA estimation for nested polarization sensitive array in unknown nonuniform noise environment[J]. Signal Processing, 2020, 175: 107630. doi: 10.1016/j.sigpro.2020.107630 [23] HAN Keyong and NEHORAI A. Nested vector-sensor array processing via tensor modeling[J]. IEEE Transactions on Signal Processing, 2014, 62(10): 2542–2553. doi: 10.1109/TSP.2014.2314437 [24] SHI Zhiguo, ZHOU Chengwei, GU Yujie, et al. Source estimation using coprime array: A sparse reconstruction perspective[J]. IEEE Sensors Journal, 2017, 17(3): 755–765. doi: 10.1109/JSEN.2016.2637059 [25] 周成伟, 郑航, 顾宇杰, 等. 互质阵列信号处理研究进展: 波达方向估计与自适应波束成形[J]. 雷达学报, 2019, 8(5): 558–577. doi: 10.12000/JR19068ZHOU Chengwei, ZHENG Hang, GU Yujie, et al. Research progress on coprime array signal processing: Direction-of-Arrival estimation and adaptive beamforming[J]. Journal of Radars, 2019, 8(5): 558–577. doi: 10.12000/JR19068 [26] ZHENG Hang, SHI Zhiguo, ZHOU Chengwei, et al. Coupled coarray tensor CPD for DOA estimation with coprime L-shaped array[J]. IEEE Signal Processing Letters, 2021, 28: 1545–1549. doi: 10.1109/LSP.2021.3099074 [27] SHEN Yifan, ZHOU Chengwei, GU Yujie, et al. Vandermonde decomposition of coprime coarray covariance matrix for DOA estimation[C]. 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications, Sapporo, Japan, 2017: 1–5. [28] YANG Minglei, DING Jin, CHEN Baixiao, et al. A multiscale sparse array of spatially spread electromagnetic-vector-sensors for direction finding and polarization estimation[J]. IEEE Access, 2018,, 6: 9807–9818. doi: 10.1109/ACCESS.2018.2799905 [29] LECUN Y, BENGIO Y, and HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436–444. doi: 10.1038/nature14539 [30] ZHOU Xingyue, YAN Yunde, and DUAN Rui. Deep learning based on striation images for underwater and surface target classification[J]. IEEE Signal Processing Letters, 2019, 26(9): 1378–1382. doi: 10.1109/LSP.2019.2919102 [31] ZHOU Xingyue, YAN Yonghong, and YANG Kunde. A multi-feature compression and fusion strategy of vertical self-contained hydrophone array[J]. IEEE Sensors Journal, 2021, 21(21): 24349–24358. doi: 10.1109/JSEN.2021.3112164 [32] 朱圣棋, 余昆, 许京伟, 等. 波形分集阵列新体制雷达研究进展与展望[J]. 雷达学报, 2021, 10(6): 795–810. doi: 10.12000/JR21188ZHU Shengqi, YU Kun, XU Jingwei, et al. Research progress and prospect for the noval waveform diverse array radar[J]. Journal of Radars, 2021, 10(6): 795–810. doi: 10.12000/JR21188 [33] TIAN Jianghao, CAO Xiangyu, GAO Jun, et al. A reconfigurable ultra-wideband polarization converter based on metasurface incorporated with PIN diodes[J]. Journal of Applied Physics, 2019, 125(13): 135105. doi: 10.1063/1.5067383 [34] 于惠存, 曹祥玉, 高军, 等. 一种宽带可重构反射型极化旋转表面[J]. 物理学报, 2018, 67(22): 224101. doi: 10.7498/aps.67.20181041YU Huicun, CAO Xiangyu, GAO Jun, et al. Broadband reconfigurable reflective polarization convertor[J]. Acta Physica Sinica, 2018, 67(22): 224101. doi: 10.7498/aps.67.20181041 [35] YU Huicun, CAO Xiangyu, GAO Jun, et al. Design of a wideband and reconfigurable polarization converter using a manipulable metasurface[J]. Optical Materials Express, 2018, 8(11): 3373–3381. doi: 10.1364/OME.8.003373 [36] MOLERO C, PALOMARES-CABALLERO Á, ALEX-AMOR A, et al. Metamaterial-based reconfigurable intelligent surface: 3D meta-atoms controlled by graphene structures[J]. IEEE Communications Magazine, 2021, 59(6): 42–48. doi: 10.1109/MCOM.001.2001161 [37] WONG K T. Direction finding/polarization estimation-dipole and/or loop triad(s)[J]. IEEE Transactions on Aerospace and Electronic Systems, 2001, 37(2): 679–684. doi: 10.1109/7.937478 [38] WONG K T and YUAN Xin. “Vector cross-product direction-finding” with an electromagnetic vector-sensor of six orthogonally oriented but spatially noncollocating dipoles/loops[J]. IEEE Transactions on Signal Processing, 2011, 59(1): 160–171. doi: 10.1109/TSP.2010.2084085 [39] YUE Yaxing, XU Yougen, ZHUANG Junpeng, et al. Mutual coupling self-calibration for parameter estimation with vector antennas[C]. 2019 IEEE International Conference on Signal, Information and Data Processing, Chongqing, China, 2019: 1–5. [40] YUE Yaxing, XU Yougen, and LIU Zhiwen. Closed-form two-dimensional DOA and polarization estimation of coexisted circular and noncircular signals[C]. CIE Radar Conference, Haikou, China, 2021: 1556–1560. [41] GONG Xiaofeng, JIANG Jiacheng, LI Hui, et al. Spatially spread dipole/loop quint for vector-cross-product-based direction finding and polarisation estimation[J]. IET Signal Processing, 2018, 12(5): 636–642. doi: 10.1049/iet-spr.2017.0232 [42] ZOLTOWSKI M D and WONG K T. ESPRIT-based 2-D direction finding with a sparse uniform array of electromagnetic vector sensors[J]. IEEE Transactions on Signal Processing, 2000, 48(8): 2195–2204. doi: 10.1109/78.852000 [43] 司伟建, 周炯赛, 曲志昱. 稀疏极化敏感阵列的波达方向和极化参数联合估计[J]. 电子与信息学报, 2016, 38(5): 1129–1134. doi: 10.11999/JEIT150840SI Jianwei, ZHOU Jiongsai, and QU Zhiyu. Joint DOA and polarization estimation with sparsely distributed polarization sensitive array[J]. Journal of Electronics &Information Technology, 2016, 38(5): 1129–1134. doi: 10.11999/JEIT150840 [44] 马慧慧, 陶海红. 稀疏拉伸式L型极化敏感阵列的二维波达方向和极化参数联合估计[J]. 电子与信息学报, 2020, 42(4): 902–909. doi: 10.11999/JEIT190208MA Huihui and TAO Haihong. Joint 2D-DOA and polarization parameter estimation with sparsely stretched l-shaped polarization sensitive array[J]. Journal of Electronics &Information Technology, 2020, 42(4): 902–909. doi: 10.11999/JEIT190208 [45] ZHENG Guimei. Two-dimensional DOA estimation for polarization sensitive array consisted of spatially spread crossed-dipole[J]. IEEE Sensors Journal, 2018, 18(12): 5014–5023. doi: 10.1109/JSEN.2018.2820168 [46] JOSHI S and BOYD S. Sensor selection via convex optimization[J]. IEEE Transactions on Signal Processing, 2009, 57(2): 451–462. doi: 10.1109/TSP.2008.2007095 [47] TOHIDI E, COUTINO M, CHEPURI S P, et al. Sparse antenna and pulse placement for colocated MIMO radar[J]. IEEE Transactions on Signal Processing, 2019, 67(3): 579–593. doi: 10.1109/TSP.2018.2881656 [48] WANG Xiangrong, ABOUTANIOS E, and AMIN M G. Adaptive array thinning for enhanced DOA estimation[J]. IEEE Signal Processing Letters, 2015, 22(7): 799–803. doi: 10.1109/LSP.2014.2370632 [49] ELBIR A M and MISHRA K V. Joint antenna selection and hybrid beamformer design using unquantized and quantized deep learning networks[J]. IEEE Transactions on Wireless Communications, 2020, 19(3): 1677–1688. doi: 10.1109/TWC.2019.2956146 [50] ELBIR A M and MISHRA K V. Deep learning design for joint antenna selection and hybrid beamforming in massive MIMO[C]. 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, Atlanta, USA, 2019: 1585–1586. [51] ZHANG Shunbo, ZHANG Shun, GAO Feifei, et al. Deep learning optimized sparse antenna activation for reconfigurable intelligent surface assisted communication[J]. IEEE Transactions on Communications, 2021, 69(10): 6691–6705. doi: 10.1109/TCOMM.2021.3097726 [52] WANDALE S and ICHIGE K. Design of sparse arrays via deep learning for enhanced DOA estimation[J]. EURASIP Journal on Advances in Signal Processing, 2021, 2021(1): 17. doi: 10.1186/S13634-021-00727-5 [53] BLISS D W and FORSYTHE K W. Multiple-input multiple-output (MIMO) radar and imaging: Degrees of freedom and resolution[C]. The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, Pacific Grove, USA, 2003: 54–59. [54] JIN Ming, LIAO Guisheng, and LI Jun. Joint DOD and DOA estimation for bistatic MIMO radar[J]. Signal Processing, 2009, 89(2): 244–251. doi: 10.1016/j.sigpro.2008.08.003 [55] ZHANG Xiaofei, XU Lingyun, XU Lei, et al. Direction of departure (DOD) and direction of arrival (DOA) estimation in MIMO radar with reduced-dimension MUSIC[J]. IEEE Communications Letters, 2010, 14(12): 1161–1163. doi: 10.1109/LCOMM.2010.102610.101581 [56] YUE Yaxing, XU Yougen, and LIU Zhiwen. Two-dimensional direction-of-arrival estimation in monostatic MIMO radar[C]. 2021 4th International Conference on Information Communication and Signal Processing, Shanghai, China, 2021: 60–64. [57] HASSANIEN A and VOROBYOV S A. Transmit energy focusing for DOA estimation in MIMO radar with colocated antennas[J]. IEEE Transactions on Signal Processing, 2011, 59(6): 2669–2682. doi: 10.1109/TSP.2011.2125960 [58] BENCHEIKH M L, WANG Yide, and HE Hongyang. Polynomial root finding technique for joint DOA DOD estimation in bistatic MIMO radar[J]. Signal Processing, 2010, 90(9): 2723–2730. doi: 10.1016/j.sigpro.2010.03.023 [59] LI Jianfeng, HE Yi, HE Lang, et al. DOD and DOA estimation for MIMO radar based on combined MUSIC and sparse Bayesian learning[C]. 2019 International Applied Computational Electromagnetics Society Symposium-China (ACES), Nanjing, China, 2019: 1–2. [60] BAIDOO E, HU Jurong, ZENG Bao, et al. Joint DOD and DOA estimation using tensor reconstruction based sparse representation approach for bistatic MIMO radar with unknown noise effect[J]. Signal Processing, 2021, 182: 107912. doi: 10.1016/j.sigpro.2020.107912 [61] LIU Yang, CHAI Jin, ZHANG Yinghui, et al. Low-complexity neural network based DOA estimation for wideband signals in massive MIMO systems[J]. AEU-International Journal of Electronics and Communications, 2021, 138: 153853. doi: 10.1016/J.AEUE.2021.153853 [62] MOLAEI A M, DEL HOUGNE P, FUSCO V, et al. Efficient joint estimation of DOA, range and reflectivity in near-field by using mixed-order statistics and a symmetric MIMO array[J]. IEEE Transactions on Vehicular Technology, 2022, 71(3): 2824–2842. doi: 10.1109/TVT.2021.3138251 [63] CHEN Chunyang and VAIDYANATHAN P P. Minimum redundancy MIMO radars[C]. 2008 IEEE International Symposium on Circuits and Systems, Seattle, USA, 2008: 45–48. [64] HUANG Yan, LIAO Guisheng, LI Jun, et al. Sum and difference coarray based MIMO radar array optimization with its application for DOA estimation[J]. Multidimensional Systems and Signal Processing, 2017, 28(4): 1183–1202. doi: 10.1007/s11045-016-0387-2 [65] ZHANG Yule, HU Guoping, ZHOU Hao, et al. DOA estimation of a novel generalized nested MIMO radar with high degrees of freedom and hole-free difference coarray[J]. Mathematical Problems in Engineering, 2021, 2021: 6622154. doi: 10.1155/2021/6622154 [66] YANG Minglei, SUN Lei, YUAN Xin, et al. A new nested MIMO array with increased degrees of freedom and hole-free difference coarray[J]. IEEE Signal Processing Letters, 2018, 25(1): 40–44. doi: 10.1109/lsp.2017.2766294 [67] LIU Donglei, ZHAO Yongbo, and DONG Shuxian. A novel co-prime MIMO radar model for DOA estimation[J]. Signal Processing, 2022, 199: 108606. doi: 10.1016/j.sigpro.2022.108606 [68] ZHANG Fei, JI Chuantang, ZHANG Zijing, et al. Non-circular signal DOA estimation based on coprime array MIMO radar[J]. EURASIP Journal on Advances in Signal Processing, 2021(1): 99. doi: 10.1186/S13634-021-00806-7 [69] SHI Junpeng, HU Guoping, ZHANG Xiaofei, et al. Sparsity-based DOA estimation of coherent and uncorrelated targets with flexible MIMO radar[J]. IEEE Transactions on Vehicular Technology, 2019, 68(6): 5835–5848. doi: 10.1109/TVT.2019.2913437 [70] WEN Fangqing, SHI Junpeng, and ZHANG Zijing. Joint 2D-DOD, 2D-DOA, and polarization angles estimation for bistatic EMVS-MIMO radar via PARAFAC analysis[J]. IEEE Transactions on Vehicular Technology, 2020, 69(2): 1626–1638. doi: 10.1109/TVT.2019.2957511 [71] DING Xueke, HU Ying, LIU Changming, et al. Coherent targets parameter estimation for EVS-MIMO radar[J]. Remote Sensing, 2022, 14(17): 4331. doi: 10.3390/rs14174331 [72] PONNUSAMY P, SUBRAMANIAM K, and CHINTAGUNTA S. Computationally efficient method for joint DOD and DOA estimation of coherent targets in MIMO radar[J]. Signal Processing, 2019, 165: 262–267. doi: 10.1016/j.sigpro.2019.07.015 [73] WANG Xianpeng, HUANG Mengxing, and WAN Liangtian. Joint 2D-DOD and 2D-DOA estimation for coprime EMVS–MIMO radar[J]. Circuits, Systems, and Signal Processing, 2021, 40(6): 2950–2966. doi: 10.1007/s00034-020-01605-5 [74] YANG Yongqiang, RUAN Ningjun, HUANG Guanjun, et al. A propagator method for bistatic coprime EMVS-MIMO radar[J]. Mathematical Problems in Engineering, 2021, 2021: 9954573. doi: 10.1155/2021/9954573 [75] 谢前朋, 潘小义, 陈吉源, 等. 基于新型阵列的双基地电磁矢量传感器MIMO雷达高分辨角度参数估计[J]. 电子与信息学报, 2021, 43(2): 270–276. doi: 10.11999/JEIT200130XIE Qianpeng, PAN Xiaoyi, CHEN Jiyuan, et al. High resolution angle parameter estimation for bistatic EMVS-MIMO radar based on a new designed array[J]. Journal of Electronics &Information Technology, 2021, 43(2): 270–276. doi: 10.11999/JEIT200130 [76] 赵永波, 刘宏伟. MIMO雷达技术综述[J]. 数据采集与处理, 2018, 33(3): 389–399. doi: 10.16337/j.1004-9037.2018.03.001ZHAO Yongbo and LIU Hongwei. Overview on MIMO radar[J]. Journal of Data Acquisition and Processing, 2018, 33(3): 389–399. doi: 10.16337/j.1004-9037.2018.03.001 [77] ANTONIK P, WICKS M C, GRIFFITHS H D, et al. Range-dependent beamforming using element level waveform diversity[C]. 2006 International Waveform Diversity & Design Conference, Lihue, USA, 2006: 1–6. [78] ANTONIK P, WICKS M C, GRIFFITHS H D, et al. Frequency diverse array radars[C]. 2006 IEEE Conference on Radar, Verona, USA, 2006: 3. [79] WICKS M C and ANTONIK P. Frequency diverse array with independent modulation of frequency, amplitude, and phase[P]. US 7319427, 2008. [80] WICKS M C and ANTONIK P. Method and apparatus for a frequency diverse array[P]. US 7511665, 2009. [81] ANTONIK P. An investigation of a frequency diverse array[D]. [Ph. D. dissertation], University College London, 2009. [82] BASIT A, WANG Wenqin, NUSENU S Y, et al. FDA based QSM for mmwave wireless communications: Frequency diverse transmitter and reduced complexity receiver[J]. IEEE Transactions on Wireless Communications, 2021, 20(7): 4571–4584. doi: 10.1109/TWC.2021.3060512 [83] NUSENU S Y, SHAO Huaizong, PAN Ye, et al. Directional modulation with precise legitimate location using time-modulation retrodirective frequency diversity array for secure IoT communications[J]. IEEE Systems Journal, 2021, 15(1): 1109–1119. doi: 10.1109/JSYST.2020.3010787 [84] SAMMARTINO P F, BAKER C J, and GRIFFITHS H D. Frequency diverse MIMO techniques for radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(1): 201–222. doi: 10.1109/TAES.2013.6404099 [85] 许京伟, 朱圣棋, 廖桂生, 等. 频率分集阵雷达技术探讨[J]. 雷达学报, 2018, 7(2): 167–182. doi: 10.12000/JR18023XU Jingwei, ZHU Shengqi, LIAO Guisheng, et al. An overview of frequency diverse array radar technology[J]. Journal of Radars, 2018, 7(2): 167–182. doi: 10.12000/JR18023 [86] SUN Yan, ZHENG Zhi, WANG Wenqin, et al. DOA estimation and tracking for FDA-MIMO radar signal[J]. Digital Signal Processing, 2020, 106: 102858. doi: 10.1016/j.dsp.2020.102858 [87] XU Tengxian, WANG Xianpeng, HUANG Mengxing, et al. Tensor-based reduced-dimension music method for parameter estimation in monostatic FDA-MIMO radar[J]. Remote Sensing, 2021, 13(18): 3772. doi: 10.3390/rs13183772 [88] CUI Can, XU Jian, GUI Ronghua, et al. Search-free DOD, DOA and range estimation for bistatic FDA-MIMO radar[J]. IEEE Access, 2018, 6: 15431–15445. doi: 10.1109/ACCESS.2018.2816780 [89] LIU Yibin, WANG Chunyang, ZHENG Guimei, et al. Joint range and angle estimation of low-elevation target with bistatic meter-wave FDA-MIMO radar[J]. Digital Signal Processing, 2022, 127: 103556. doi: 10.1016/j.dsp.2022.103556 [90] 王文钦, 陈慧, 郑植, 等. 频控阵雷达技术及其应用研究进展[J]. 雷达学报, 2018, 7(2): 153–166. doi: 10.12000/JR18029WANG Wenqin, CHEN Hui, ZHENG Zhi, et al. Advances on frequency diverse array radar and its applications[J]. Journal of Radars, 2018, 7(2): 153–166. doi: 10.12000/JR18029 [91] 陈阳, 田波, 王春阳, 等. FDA-MIMO抗干扰技术进展及前景展望[J]. 电光与控制, 2022, 29(8): 65–72. doi: 10.3969/j.issn.1671-637X.2022.08.012CHEN Yang, TIAN Bo, WANG Chunyang, et al. Progress and prospect of FDA-MIMO anti-jamming technology[J]. Electronics Optics Control, 2022, 29(8): 65–72. doi: 10.3969/j.issn.1671-637X.2022.08.012 [92] LAN Lan, XU Jingwei, LIAO Guisheng, et al. Suppression of mainbeam deceptive jammer with FDA-MIMO radar[J]. IEEE Transactions on Vehicular Technology, 2020, 69(10): 11584–11598. doi: 10.1109/TVT.2020.3014689 [93] 兰岚, 廖桂生, 许京伟, 等. FDA-MIMO雷达主瓣距离欺骗式干扰抑制方法[J]. 系统工程与电子技术, 2018, 40(5): 997–1003. doi: 10.3969/j.issn.1001-506X.2018.05.06LAN Lan, LIAO Guisheng, XU Jingwei, et al. Main-beam range deceptive jamming suppression approach with FDA-MIMO radar[J]. Systems Engineering and Electronics, 2018, 40(5): 997–1003. doi: 10.3969/j.issn.1001-506X.2018.05.06 [94] 高霞, 全英汇, 李亚超, 等. 基于BSS的FDA-MIMO雷达主瓣欺骗式干扰抑制方法[J]. 系统工程与电子技术, 2020, 42(9): 1927–1934. doi: 10.3969/j.issn.1001-506X.2020.09.07GAO Xia, QUAN Yinghui, LI Yachao, et al. Main-lobe deceptive jamming suppression with FDA-MIMO radar based on BSS[J]. Systems Engineering and Electronics, 2020, 42(9): 1927–1934. doi: 10.3969/j.issn.1001-506X.2020.09.07 [95] 许京伟, 廖桂生, 张玉洪, 等. 波形分集阵雷达抗欺骗式干扰技术[J]. 电子学报, 2019, 47(3): 545–551. doi: 10.3969/j.issn.0372-2112.2019.03.005XU Jingwei, LIAO Guisheng, ZHANG Yuhong, et al. On anti-jamming technique with waveform diverse array radar[J]. Acta Electronica Sinica, 2019, 47(3): 545–551. doi: 10.3969/j.issn.0372-2112.2019.03.005 [96] 陈浩, 李荣锋, 戴凌燕, 等. 基于 FVE 法的 FDA-MIMO 雷达主瓣密集假目标干扰抑制[J]. 空军预警学院学报, 2018, 32(6): 397–401. doi: 10.3969/j.issn.2095-5839.2018.06.002CHEN Hao, LI Rongfeng, DAI Lingyan, et al. FDA-MIMO radar mainlobe dense false target jamming suppression based on feature vector eliminating[J]. Journal of Air Force Early Warning Academy, 2018, 32(6): 397–401. doi: 10.3969/j.issn.2095-5839.2018.06.002 [97] QIN Si, ZHANG Y D, AMIN M G, et al. Frequency diverse coprime arrays with coprime frequency offsets for multitarget localization[J]. IEEE Journal of Selected Topics in Signal Processing, 2017, 11(2): 321–335. doi: 10.1109/JSTSP.2016.2627184 [98] CAO Ruisong, LIU Shengheng, MAO Zihuan, et al. Doubly-Toeplitz-based interpolation for joint DOA-range estimation using coprime FDA[C]. 2021 IEEE Radar Conference, Atlanta, USA, 2021: 1–6. [99] SEDIGHI S, SHANKAR B, MISHRA K V, et al. Optimum design for sparse FDA-MIMO automotive radar[C]. 2019 53rd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, USA, 2019: 913–918. [100] LI Binbin, BAI Weixiong, and ZHENG Guimei. Successive ESPRIT algorithm for joint DOA-range-polarization estimation with polarization sensitive FDA-MIMO radar[J]. IEEE Access, 2018, 6: 36376–36382. doi: 10.1109/ACCESS.2018.2844948 [101] LI Binbin, CHEN Hui, ZHENG Guimei, et al. Joint DOA-range-polarization estimation with polarization sensitive FDA-MIMO radar[C]. International Conference on Frontiers of Electronics, Information and Computation Technologies, Changsha, China, 2021: 22. [102] YU Nanfang, GENEVET P, KATS M A, et al. Light propagation with phase discontinuities: Generalized laws of reflection and refraction[J]. Science, 2011, 334(6054): 333–337. doi: 10.1126/science.1210713 [103] 杨欢欢, 曹祥玉, 高军, 等. 可重构电磁超表面及其应用研究进展[J]. 雷达学报, 2021, 10(2): 206–219. doi: 10.12000/JR20137YANG Huanhuan, CAO Xiangyu, GAO Jun, et al. Recent advances in reconfigurable metasurfaces and their applications[J]. Journal of Radars, 2021, 10(2): 206–219. doi: 10.12000/JR20137 [104] YANG Wanchen, CHEN Si, CHE Wenquan, et al. Compact high-gain metasurface antenna arrays based on higher-mode SIW cavities[J]. IEEE Transactions on Antennas and Propagation, 2018, 66(9): 4918–4923. doi: 10.1109/TAP.2018.2851659 [105] NIE Niansheng, YANG Xuesong, CHEN Zhining, et al. A low-profile wideband hybrid metasurface antenna array for 5G and WiFi systems[J]. IEEE Transactions on Antennas and Propagation, 2020, 68(2): 665–671. doi: 10.1109/TAP.2019.2940367 [106] YANG Wanchen, MENG Qian, CHE Wenquan, et al. Low-profile wideband dual-circularly polarized metasurface antenna array with large beamwidth[J]. IEEE Antennas and Wireless Propagation Letters, 2018, 17(9): 1613–1616. doi: 10.1109/LAWP.2018.2857625 [107] CUI Tiejun, QI Meiqing, WAN Xiang, et al. Coding metamaterials, digital metamaterials and programmable metamaterials[J]. Light:Science & Applications, 2014, 3(10): e218. doi: 10.1038/lsa.2014.99 [108] YANG Huanhuan, YANG Fan, CAO Xiangyu, et al. A 1600-element dual-frequency electronically reconfigurable reflectarray at X/Ku-band[J]. IEEE transactions on antennas and propagation, 2017, 65(6): 3024–3032. doi: 10.1109/TAP.2017.2694703 [109] BASAR E, DI RENZO M, DE ROSNY J, et al. Wireless communications through reconfigurable intelligent surfaces[J]. IEEE Access, 2019, 7: 116753–116773. doi: 10.1109/ACCESS.2019.2935192 [110] GUAN Xinrong, WU Qingqing, and ZHANG Rui. Intelligent reflecting surface assisted secrecy communication: Is artificial noise helpful or not?[J]. IEEE Wireless Communications Letters, 2020, 9(6): 778–782. doi: 10.1109/LWC.2020.2969629 [111] DI Boya, ZHANG Hongliang, SONG Lingyang, et al. Hybrid beamforming for reconfigurable intelligent surface based multi-user communications: Achievable rates with limited discrete phase shifts[J]. IEEE Journal on Selected Areas in Communications, 2020, 38(8): 1809–1822. doi: 10.1109/JSAC.2020.3000813 [112] LI Wenting, GAO S, CAI Yuanming, et al. Polarization-reconfigurable circularly polarized planar antenna using switchable polarizer[J]. IEEE Transactions on Antennas and Propagation, 2017, 65(9): 4470–4477. doi: 10.1109/TAP.2017.2730240 [113] MA Xiaoliang, PAN Wenbo, HUANG Cheng, et al. An active metamaterial for polarization manipulating[J]. Advanced Optical Materials, 2014, 2(10): 945–949. doi: 10.1002/adom.201400212 [114] CUI Jianhua, HUANG Cheng, PAN Wenbo, et al. Dynamical manipulation of electromagnetic polarization using anisotropic meta-mirror[J]. Scientific Reports, 2016, 6(1): 30771. doi: 10.1038/srep30771 [115] TAO Zui, WAN Xiang, PAN Baicao, et al. Reconfigurable conversions of reflection, transmission, and polarization states using active metasurface[J]. Applied Physics Letters, 2017, 110(12): 121901. doi: 10.1063/1.4979033 [116] ZHANG Meng, ZHANG W, LIU A Q, et al. Tunable polarization conversion and rotation based on a reconfigurable metasurface[J]. Scientific Reports, 2017, 7(1): 12068. doi: 10.1038/s41598-017-11953-z [117] ELMOSSALLAMY M A, ZHANG Hongliang, SONG Lingyang, et al. Reconfigurable intelligent surfaces for wireless communications: Principles, challenges, and opportunities[J]. IEEE Transactions on Cognitive Communications and Networking, 2020, 6(3): 990–1002. doi: 10.1109/TCCN.2020.2992604 [118] Alexandropoulos G C, Shlezinger N, and Del Hougne P. Reconfigurable intelligent surfaces for rich scattering wireless communications: Recent experiments, challenges, and opportunities[J]. IEEE Communications Magazine, 2021, 59(6): 28–34. doi: 10.1109/MCOM.001.2001117 [119] HUANG Chongwen, ZAPPONE A, ALEXANDROPOULOS G C, et al. Reconfigurable intelligent surfaces for energy efficiency in wireless communication[J]. IEEE Transactions on Wireless Communications, 2019, 18(8): 4157–4170. doi: 10.1109/TWC.2019.2922609 [120] BJÖRNSON E, ÖZDOGAN Ö, and LARSSON E G. Reconfigurable intelligent surfaces: Three myths and two critical questions[J]. IEEE Communications Magazine, 2020, 58(12): 90–96. doi: 10.1109/MCOM.001.2000407 [121] DI RENZO M, ZAPPONE A, DEBBAH M, et al. Smart radio environments empowered by reconfigurable intelligent surfaces: How it works, state of research, and the road ahead[J]. IEEE Journal on Selected Areas in Communications, 2020, 38(11): 2450–2525. doi: 10.1109/JSAC.2020.3007211 [122] HU Xiaoling, ZHONG Caijun, ZHANG Yu, et al. Location information aided multiple intelligent reflecting surface systems[J]. IEEE Transactions on Communications, 2020, 68(12): 7948–7962. doi: 10.1109/TCOMM.2020.3020577 [123] LIN Shaoe, ZHENG Beixiong, ALEXANDROPOULOS G C, et al. Adaptive transmission for reconfigurable intelligent surface-assisted OFDM wireless communications[J]. IEEE Journal on Selected Areas in Communications, 2020, 38(11): 2653–2665. doi: 10.1109/JSAC.2020.3007038 [124] ZENG Ming, LI Xingwang, LI Gen, et al. Sum rate maximization for IRS-assisted uplink NOMA[J]. IEEE Communications Letters, 2021, 25(1): 234–238. doi: 10.1109/LCOMM.2020.3025978 [125] ZUO Jiakuo, LIU Yuanwei, BASAR E, et al. Intelligent reflecting surface enhanced millimeter-wave NOMA systems[J]. IEEE Communications Letters, 2020, 24(11): 2632–2636. doi: 10.1109/LCOMM.2020.3009158 [126] WU Qingqing and ZHANG Rui. Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming[J]. IEEE Transactions on Wireless Communications, 2019, 18(11): 5394–5409. doi: 10.1109/TWC.2019.2936025 [127] TANG Wankai, DAI Junyan, CHEN Mingzheng, et al. MIMO transmission through reconfigurable intelligent surface: System design, analysis, and implementation[J]. IEEE Journal on Selected Areas in Communications, 2020, 38(11): 2683–2699. doi: 10.1109/JSAC.2020.3007055 [128] WU Qingqing and ZHANG Rui. Towards smart and reconfigurable environment: Intelligent reflecting surface aided wireless network[J]. IEEE Communications Magazine, 2020, 58(1): 106–112. doi: 10.1109/MCOM.001.1900107 [129] CHEN Peng, CHEN Zhimin, ZHENG Beixiong, et al. Efficient DOA estimation method for reconfigurable intelligent surfaces aided UAV swarm[J]. IEEE Transactions on Signal Processing, 2022, 70: 743–755. doi: 10.1109/TSP.2022.3146791 [130] ZHANG Jiliang, GLAZUNOV A A, YANG Wenfei, et al. Fundamental wireless performance of a building[J]. IEEE Wireless Communications, 2022, 29(1): 186–193. doi: 10.1109/MWC.121.2100244 [131] ZHANG Jiliang, GLAZUNOV A A, and ZHANG Jie. Wireless performance evaluation of building layouts: Closed-form computation of figures of merit[J]. IEEE Transactions on Communications, 2021, 69(7): 4890–4906. doi: 10.1109/TCOMM.2021.3074546 [132] ANDREWS J G, ZHANG Xinchen, DURGIN G D, et al. Are we approaching the fundamental limits of wireless network densification?[J]. IEEE Communications Magazine, 2016, 54(10): 184–190. doi: 10.1109/MCOM.2016.7588290 [133] CALABUIG D, BARMPOUNAKIS S, GIMENEZ S, et al. Resource and mobility management in the network layer of 5G cellular ultra-dense networks[J]. IEEE Communications Magazine, 2017, 55(6): 162–169. doi: 10.1109/MCOM.2017.1600293 [134] BUSARI S A, HUQ K M S, MUMTAZ S, et al. Millimeter-wave massive MIMO communication for future wireless systems: A survey[J]. IEEE Communications Surveys & Tutorials, 2018, 20(2): 836–869. doi: 10.1109/COMST.2017.2787460 [135] ZENG Shuhao, ZHANG Hongliang, DI Boya, et al. Reconfigurable intelligent surface (RIS) assisted wireless coverage extension: RIS orientation and location optimization[J]. IEEE Communications Letters, 2021, 25(1): 269–273. doi: 10.1109/LCOMM.2020.3025345 [136] PENA D, FEICK R, HRISTOV H D, et al. Measurement and modeling of propagation losses in brick and concrete walls for the 900-MHz band[J]. IEEE Transactions on Antennas and Propagation, 2003, 51(1): 31–39. doi: 10.1109/TAP.2003.808539 [137] ZHANG Jiliang, LIAO Xi, GLAZUNOV A A, et al. Two-ray reflection resolution algorithm for planar material electromagnetic property measurement at the millimeter-wave bands[J]. Radio Science, 2020, 55(3): e2019RS006944. doi: 10.1029/2019RS006944 [138] ZHOU Yang, SHAO Yu, ZHANG Jiliang, et al. Wireless performance evaluation of building materials integrated with antenna arrays[J]. IEEE Communications Letters, 2022, 26(4): 942–946. doi: 10.1109/LCOMM.2022.3141390 期刊类型引用(4)
1. 王昊，张敏，张振华，胡继军，刘平原，刘少坤，纪永刚. 基于无人机集群的综合一体化告警自卫系统设计. 现代电子技术. 2024(07): 77-82 . 
百度学术2. 李洪爱，郑尧，孙伟，齐季，张闰秋. 基于直升机载雷达告警设备的无源定位技术. 计算机与网络. 2024(03): 272-275 . 百度学术3. 王星斗，坤娅. 基于脉冲重复间隔的信号分选方法研究. 航空计算技术. 2024(06): 55-59 . 百度学术4. 吴礼洋，呙鹏程，刘超，李文强. 基于注意力机制增强残差网络的雷达信号调制类型识别. 兵工学报. 2023(08): 2310-2318 . 百度学术其他类型引用(6)
-
下载:
百度学术
计量
- 文章访问数:
- HTML全文浏览量:
- PDF下载量:
- 被引次数: 10
