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Overview and Prospects of Research on Sea Clutter Property Cognition
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摘要: 海杂波是影响海用雷达目标探测性能的主要制约因素之一，其物理机理复杂，影响因素众多，且非高斯、非平稳特性显著，因此海杂波特性认知研究是一项极其复杂的系统工程。该文从数据层海杂波特性认知出发，围绕目标检测算法所关注的海杂波幅度分布特性、谱特性、相关性及非平稳与非线性特性，回顾和总结了海杂波特性认知研究进展，梳理了主要研究结论。在此基础上，从海杂波影响因素的深化分析、海杂波精细化建模与检测器需求的博弈、海杂波与目标差异特性认知等4个方面展望了有待于进一步探索的问题。Abstract: Sea clutter is one of the main limiting factors influencing the target detection performance of nautical radars. The physical mechanism of sea clutter is complex with an abundance of influencing factors, and the non-Gaussian as well as non-stationarity behavior is significant. Thus, research into sea clutter property cognition is complicated and has to be systematic. Based on research that concentrates on experimental data, this paper reviews and summarizes the research developments in sea clutter property cognition. It concentrates on the properties that are of most interest for target detection algorithms:amplitude distribution, spectra, correlation, and non-stationarity and nonlinearity. The main research results are also concluded. Based on this, four aspects of problems that need further exploration are highlighted and include the following:further analysis of sea clutter influencing factors; the game problem between sea clutter precision modeling and the requirements of detection algorithms; and the property cognition between radar target and sea clutter.
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Key words:
- Sea clutter /
- Property cognition /
- Non-Gaussian /
- Non-stationarity /
- Precision modeling /
- Target detection
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1. 引言
电磁超表面被视为电磁超材料的二维形式，它可以在表面处对电磁波的特性有效调控，从而突破自然材料表面的Snell定律限制，实现各种奇异的电磁现象[1,2]。2011年，美国哈佛大学Yu等人[3]在Science发文首次提出超表面的概念，介绍了利用超表面实现对连续光波的调控，并将光波在超表面的反射和折射规律归纳为广义Snell定律。与三维的超材料相比，超表面的亚波长厚度，使其可以采用光刻、印刷等大规模生产方式制备，也可直接和电路集成应用，这些突出优势极大激发了研究人员的兴趣。起初，超表面的研究集中在无源设计，一旦制备完成后，其功能是固定的，这在某种程度上限制了其适用范围。2014年，东南大学Cui等人[4]和美国宾夕法尼亚大学Giovam paola等人[5]相互独立地提出数字超材料的概念。与Giovam paola等人相比，崔铁军[6]利用数字编码代替等效媒质描述超材料，并首次提出可编程超材料的概念，构建了超材料研究的新体系。他们还利用可编程超表面实现了微波频段对平面波的动态调控，可调谐超表面随之进入研究人员的视线并得到了迅速发展。
“可重构”一词源于计算机领域，其本意是指通过电子器件控制计算电路，在不改变电路结构的前提下实现多种可控的功能。物理学家将这一概念引入超表面领域，用可重构超表面描述具有多种可调性能的超表面。区别于早期的性能固定超表面，可重构超表面的性能可以灵活调节，这立刻吸引了众人的广泛关注。截至目前，可重构超表面的研究主要集中于3个层面：可重构单元设计与优化、可重构超表面功能与实现、基于可重构超表面的系统构建与验证。
高性能的单元是可重构超表面设计的基础和关键。以反射型超表面为例，图1给出了单元实现可重构的3种主要方式：改变谐振频率、改变传输线长度、改变旋转角度。在这3种方式的基础上可以衍生出各式各样的结构设计[7-13]。可重构单元设计与优化的难点主要在于如何将可重构技术与超表面巧妙结合以获得需要的高性能。如在电控可重构单元中，如何选择电子器件类型与数量[14]、电子器件在单元中的位置设计[15]、电子器件的偏置结构加载[16]、如何提高可重构性能(高吸波率/低损耗、宽带宽)[7,17,18]和如何拓展可重构的性能维度(双极化、多功能)[19-21]等。
利用可重构单元组成表面，可以获得多种电磁功能的捷变调控。早在2003年，Sievenpiper等人[22]通过整列控制超表面单元，获得相位梯度，实现了反射波束在±40°内二维扫描。后来，人们发现对每个单元独立控制可以获得很多重构的先进性能，实现诸如空间波束赋形[23,24]、新型相控阵[25-28]、全息成像[29,30]、涡旋波复用[31]甚至是多种功能的一体化[32]等。2016年，Cui等人[33]利用可重构超表面进行信息熵操作及数字卷积和加法，实现了对电磁波和数字信息的同时调控。近两年，从时间、空间和时域、频域维度研究时空编码超表面[34]和非线性超表面[35]使得可重构超表面的研究有了新的突破。
随着可重构超表面研究的深入，可重构超表面实时调控电磁波的能力不断丰富。这使得人们不禁思考：基于可重构超表面的系统设计能否代替传统的无线通信系统？2017年，采用1比特可重构超表面实现的实时动态可调微波全息成像系统成功实现[30]。2019年，基于可重构超表面的无线数字传输系统研制成功[36]。与经典的无线传输系统相比，这类传输系统不再需要数模转换等中间过程，而是直接将信息加载到超表面上进行辐射，极大简化了发射机的架构[37]。同年，基于可重构超表面的可认知超表面系统也被报道[38]，这将摆脱超表面控制对人工的依赖，实现智能化，加速人工智能时代的到来。
作为可重构超表面研究团队家族中的一员，空军工程大学曹祥玉教授领导的课题组从2006年起就持续关注超材料的最新进展，并开展了大量探索研究。2010年以来，团队将可重构概念和超表面技术有机结合，开展微波频段的可重构电磁超表面研究，取得了一些有益成果。本文在介绍国内外可重构超表面研究现状的基础上，以该团队的研究成果作为案例进行综述，从一个侧面见证可重构超表面的研究与发展历程，并分析了可重构超表面未来的发展方向。
2. 可重构超表面的实现技术
实现超表面可重构的技术可分为机械方式和电控方式两大类。
机械方式是通过机械改变超表面物理结构，从而获得电磁性能变化。常见的机械方式主要包括单元旋转[26]和高度平移[39]。由于实施简单，机械方式是实现可重构较早的一种方式，但由于重构速度慢、空间受限、控制复杂、精度易受机械误差影响，机械方式并未受到太多重视。近些年，随着电控机械控制器件的发展，采用直线执行器、微马达等控制超表面单元重新引起人们的关注。尤其机械方式特有的不增加单元损耗、线性度好等特点在工程上很受青睐。
电控方式通过电控器件或电控材料实现超表面性能的重构。与机械方式相比，电控方式具有响应速度快、集成度高、设计灵活等突出优点，也是可重构超表面近些年的主要实现方式[40,41]。表1总结了文献中常用的电控可重构实现技术。其中，常用的集总电子元件主要包括PIN二极管、压控/变容二极管和基于MEMS (Micro-Electro-Mechanical System)的开关/二极管。PIN二极管可以作为微波开关[42]，采用N个PIN二极管的超表面单元其重构状态往往不超过2N个；而压控/变容二极管在外加不同电压时，其PN结反偏结电容会发生连续变化，可实现超表面的连续重构状态[43]。尽管这两种集总元件技术成熟、应用广泛、响应时间快，但其在微波高频段损耗较大。MEMS技术的发展则可克服这一不足。MEMS是利用微机械技术将机械构件、驱动部件、电控系统集成为一个单元的微型系统，基于MEMS制作的开关/变容管[44,45]损耗几乎可忽略、功耗低、线性度好、工作频带宽，但受技术成熟度、成本等限制，其大规模应用仍然十分有限。功能材料也是实现超表面可重构的研究热点之一[46-50]，尤其在频率相对较高的毫米波频段，以功能材料作为超表面的介质，通过外加电压改变介质的等效电参数，可连续调整超表面的性能。
表 1 电控可重构实现技术Table 1. Technologies for the implementation of electronic control reconfigurability参数 集总元件 功能材料 PIN二极管 压控/变容二极管 射频MEMS 液晶 石墨烯 砷化镓 技术成熟度 + + 0 0 – – 偏置复杂度 – – + 0 + 控制 数字(1 V) 模拟(0～30 V) 数字(60 V) 模拟 模拟 模拟 成本 + + 0 0 0 – 损耗(微波) – – + – – – 功耗 – + + 0 + 速度 +(ns) +(ns) 0(ms) – + 线性度 0 – + 0 – 可用度 商用 商用 多数需定制 特殊设备 注：+：好；0：中；–：差。 3. 可重构超表面的电磁调控特性
电磁波可以用其振幅、相位和极化等特征量描述。当电磁波照射到可重构超表面上时，其与超表面发生作用，可以重构其振幅、相位、极化等特性。
3.1 吸波可重构超表面
吸波材料应用非常广泛。分析表明，如果吸波材料的吸波率超过90%，可以实现雷达散射截面(Radar Cross Section, RCS)减缩10 dB以上[51]。近几年雷达隐身技术的迅猛发展对吸波材料提出了新的需求和挑战。研究具有“薄、轻、宽、强”特点的吸波材料成为科学家孜孜追求的目标。超表面天生具有薄、轻的结构特点，非常契合新一代吸波材料的要求。2008年，Landy等人[52]提出并利用超表面实现了微波频段电磁波的近乎完美吸收。这类吸波体利用电谐振耦合空间波的电场能量、磁谐振耦合空间波的磁场能量，两者同时作用并结合介质损耗可实现99%的吸波率。这一成果极大鼓舞了研究人员，但超表面常伴的窄带特点也限制了吸波带宽。
2012年，本团队[53]提出利用分形结构的自相似性，在Hilbert超表面结构中焊接2个二极管，通过单片机控制二极管的开关状态，完成对吸波频段的调控，设计的结构如图2所示。经测试，该吸波体在二极管闭合时的中心工作频率为4.72 GHz，断开时的中心频率为5.32 GHz，实现了由二极管开关控制吸波频段的切换(如图3)。由于该设计中采用的二极管只有开、关两个状态，得到的强吸波频率只能在两个区间内重构。为了进一步拓展吸波频率带宽，我们在单元中集成连续可调的变容二极管，设计了如图4所示的吸波超构表面[54]。同时，为了提高吸波率，采用电阻和二极管并联的方式集成在单元结构中。图5的结果表明，该结构整体厚度为6 mm，吸波带宽覆盖0.68～2.13 GHz，与文献[7]相比，采用这种渐变形结构后，总厚度减小1.8 mm，带宽拓展了10.9%。
图 3 电可控Hilbert吸波可重构超表面反射系数Figure 3. Reflectivity of the reconfigurable Hilbert metasurface absorber3.2 相位可重构超表面
相位是电磁波矢量特性的重要表征。相位调控超表面可以对电磁波的波形灵活控制，其在辐射和散射问题中都有广泛的应用。国内外对于相位可重构超表面的研究相对较多。本团队在反射和透射型相位可重构超表面研究方面也都做了一些探索。
2015年，我们利用PIN二极管设计实现了如图6所示的一种工作在Ku波段的超薄1比特相位可重构单元[16]，并采用印刷微带线设计了具有分布参数的馈电结构。当外加电压控制二极管导通或截止时，该结构呈现出180°的反射相位差。与文献[55,56]相比，该结构具有超薄、简单易行、控制方便的特点。大量研究表明，相位可重构超表面设计中往往存在两个限制：一是带宽窄，二是损耗高。为了拓展带宽，我们进一步分析了上述超表面的谐振工作模式，通过把多个模式结合起来，仅用1个PIN管就实现了双频相位可重构单元，且证实了适当改变结构参数可以独立调控两个相位重构频率[57]。为了减小损耗，我们设计了图7所示的1比特相位可重构单元，通过加载集总电容改变二极管处的强电流分布，该单元可以在8.45～12.60 GHz范围内实现重构相位差180°±30°，同时不同状态所对应的幅度损耗均小于0.75 dB[58]。更进一步，为了同时兼顾宽带宽和低损耗，我们将MEMS技术用于超表面，设计的新型单元结构如图8所示，采用渐变贴片和增加印有贴片的覆盖层后，该结构的1比特相位重构频带为7.0～21.5 GHz，带宽达112%，且反射损耗小于0.05 dB[59]。针对近来特别关注的P和L频段超表面设计，我们从等效电路的角度分析了如何拓展可重构人工磁导体(Artifical Magnetic Conductor, AMC)的带宽，指出并联加载有源器件更有利于小型化和宽带工作，为宽带紧凑型超表面快速设计提供了方法借鉴，并设计了可以覆盖P和L频段的双极化可重构AMC[60]。
图 8 超宽带低损耗相位可重构超表面结构Figure 8. Ultra-wideband and low-loss phase reconfigurable metasurface除上述反射型相位可重构超表面之外，我们以正六边形介质为透射型超表面的单元基底，通过在印刷金属片间集成MEMS开关，得到了低损耗紧凑型1比特相位单元[61]，其对电磁波的透射结果如图9所示。为了实现对电磁波的全空间调控，我们设计了图10(a)所示的多层结构，当分别用外加电压控制第2层和第4层表面集成的PIN二极管时，该超表面实现了图10(b)所示的透射或反射相位可重构[62]，相较于文献[63]，在获得可重构性能的同时还提高了带宽。
3.3 极化可重构超表面
极化是电磁波最具特色的特征量。如图11所示，2016年，我们验证了当平面波以特定极化角照射1比特相位可重构超表面时，反射波的线极化可以在正交或者保持不变之间重构[32]。为了拓展极化重构的带宽，我们进一步设计了图12所示的超表面[64]，其由单层介质组成，单元中集成型号为RMSW200HP的MEMS开关，当MEMS开关导通时，该超表面在7.78～14.10 GHz内反射波极化与入射波正交，且极化转化率大于0.8，相对带宽为57.7%，而MEMS开关闭合时，反射波极化保持不变。
图 11 X频段超表面编码状态与极化可重构性能Figure 11. Coding matrix and polarization reconfigurable properties of the X-band metasurface
图 12 宽带线-线极化可重构超表面及其功能Figure 12. Wideband linear-to-linear polarization reconfigurable metasurface and its properties以图12所示线-线极化可重构超表面为基础，通过对结构的深入细致分析，优化得到了如图13所示的多极化可重构超表面[65]，在相同线极化波入射的条件下，随着MEMS开关由导通到断开，实现了在交叉线极化波反射和右旋圆极化波反射之间的切换，对应的频带分别为7.93～12.42 GHz (44.1%)和8.07～10.77 GHz (28.6%)。如图14，采用PIN二极管技术和多层结构，我们提出了一种新颖的设计[66]，使多极化可重构超表面的带宽进一步拓展，当二极管截止时，在7.6～23.6 GHz (102.5%)范围内，线极化的入射波以圆极化波反射，当二极管导通时，在6.5～19.9 GHz (101.5%)线极化的入射波以正交极化反射。此外，我们还尝试用MEMS开关将相邻单元直接相连，并结合多层技术，在获得宽带的同时，还实现了结构的紧凑设计，提出的单元如图15所示[67]，其正交极化反射的带宽达122.7%，圆极化反射的带宽达123.0%，且两种极化重构的频带基本一致，这些性能是目前公开报道中最优的。
图 13 宽带多极化可重构超表面及其功能Figure 13. Wideband multi-polarization reconfigurable metasurface and its properties
图 14 超宽带多极化可重构超表面单元Figure 14. Ultra-wideband multi-polarization reconfigurable metasurface unit cell
图 15 超宽带紧凑型多极化可重构超表面单元Figure 15. Ultra-wideband multi- polarization compact reconfigurable metasurface unit cell4. 可重构超表面的应用
4.1 波束可重构
从宏观结构上看，超表面最大的特点是由多个单元沿横向周期或准周期排列，这与阵列天线的外形类似。因此，研究人员甚至在超表面的概念被正式提出之前，就开始思考能否利用超表面实现阵列天线的性能，由此得到的一类高增益天线被命名为反射阵列或透射阵列。
2013年开始，我们和清华大学合作，在国内较早开始利用相位可重构超表面设计波束扫描阵列，并成功研制了国内首部电控反射阵列天线样机[28]。如图16所示，该阵列仅用1比特单元实现了±50°波束扫描，工作带宽为11.75～13.25 GHz，经过对单元和阵面的优化，该阵列的口面效率提升至17.9%，是已报道小口径1比特阵列中效率最高的设计，此外，我们还利用搭建的测试系统实测了由单片机控制的波束切换速度为12 μs。2015年，我们率先研制出国际上首个大口径双频波束扫描阵列[57]，如图17(a)，图17(b)所示，该阵列采用阵面切割拼接技术，由5个5×40子阵拼接而成，通过FPGA对每个单元实时独立控制。有趣的是，我们在阵面背后设计了与单元一一对应的LED灯，以帮助调试、判断每个单元上二极管的工作状态，这一方法后来被很多团队借鉴。图17(c)、图17(d)给出了该阵列的实测双频扫描波束。除聚焦波束之外，我们还充分挖掘该阵列的强大波束重构能力，验证了其可以实现宽波束、多波束、和差波束、赋形波束[40]，对平面波还可以形成多种赋形散射波束等[32]，如图17(e)所示。这些研究展现了相控阵的低成本化和智能隐身的光明前景。
4.2 RCS减缩
RCS是表征目标雷达隐身能力的重要指标。随着电子对抗技术的快速发展，如何减小目标的RCS成为工程实践特别是军事实践牵引出的热点问题。将超表面这一学科前沿用于解决RCS减缩这一工程问题是我们团队的一个创新思路。而更深入的研究也使我们认识到，仅仅依靠固定超表面的吸波、波束空间再分布能力实现RCS减缩往往会带来新问题，如发热易被红外设备探测、散射波束易被双基地雷达发现等。
针对该问题，2017年我们提出智能隐身超表面的概念，并与天线概念相结合，设计了图18所示的动态隐身超表面天线[68]，该天线单元中集成有可控的变容二极管，将该天线与智能处理板相连接，就可实现对照射雷达波的跳频吸收或波束捷变。新近的工程实践表明，P和L等较低频段的隐身成为新的短板和难点。为此，我们提出了图19(a)所示的新型超表面单元结构[69]，通过控制该单元中的变容二极管，整个阵列分别实现了0.90～2.19 GHz的可调辐射和0.86～1.82 GHz的动态隐身，而其剖面厚度仅有0.028λ，目前公开报道中尚未见到此类设计。
4.3 涡旋场产生
轨道角动量(Orbital Angular Momentum，OAM)涡旋电磁场作为一种潜力极大的通信手段，有望在很大程度上缓解现今频谱资源紧张、频段拥塞的问题。此外，其在成像、雷达探测等领域也有较大应用价值。我们团队研究了利用可重构电磁超表面产生多个模态涡旋场的可行性[70]，系统总结了基于可重构超表面的OAM涡旋场产生方法，揭示了平面波激励下1比特超表面产生OAM涡旋场的独特对称性[71,72]，并利用1比特反射型和透射型超表面，制作了涡旋场产生样机，实测证实了涡旋场的性能[61,73]。如图20(a)为利用1比特反射型调相超表面制作的样机实物，图20(b)为测试得到的对应涡旋场相位。
图 20 16×16可重构超表面及产生的涡旋场Figure 20. 16×16 reconfigurable metasurface and the generated vortex field5. 总结与展望
可重构技术为超表面的发展提供了新的驱动力。当下，可重构超表面的研究正如火如荼地开展。本文首先从总体上梳理了可重构超表面的发展历程及国内外的研究现状，并以空军工程大学曹祥玉教授团队为点，总结了其在可重构超表面及其应用领域所做的一些工作，主要包括吸波、相位、极化可重构超表面，及可重构超表面在阵列天线波束重构、RCS减缩及涡旋场产生方面的典型应用。限于篇幅，很多研究内容没有详细展开，具体细节可参阅参考文献。
可重构超表面作为超表面向可控、智能化、系统化发展的一个关键阶段，其前景不可估量。后续可以在以下3个方面作更深入的探索：
一是高性能的可重构超表面单元设计。目前，可重构超表面单元的研究仍面临诸多问题。从频域上看，已有研究主要集中在微波频段，且以S-X频段为主，未来的研究可朝超低频段(如P波段)和较高频段(红外、毫米波段、光波段)发展。对于低频，其设计的挑战主要在于如何获得较低的剖面；对于高频，表1中的实现方式适用性变差，因此，如何实现高频的可重构也是一个难点，也许可以结合新材料的发展找到突破。另外，谐振型超表面的可重构频带仍较窄，研究非谐振/准谐振型可重构超表面是值得探索的领域。此外，可重构超表面的非线性特性现已在前沿科学和无线通信等领域起到重要的作用，而目前对非线性效应研究刚刚起步，这涉及超表面结构自身和电子器件的非线性，其理论研究和物理实现都非常具有挑战性，将时域响应和非线性效应结合研究也为可重构超表面开辟了新的探索路线。从幅度上看，目前常用的PIN/变容二极管在较高的X频段时，其损耗急剧增大，这也给单元插入损耗的优化增加了难点。尽管MEMS技术为解决该问题带来了希望，其成本、成熟度等仍有待突破，因此如何降低插入损耗是非损耗性可重构超表面研究的课题。从性能上看，单一的可重构已无法满足未来多变的需求，实现对全空间电磁波的频率、幅度、相位和极化调控势在必行，但如何避免或利用多维度性能间的耦合效应实现想要的多性能可重构是单元设计的关键。
二是可重构超表面功能的进一步挖掘与丰富。目前的研究大致集中于两个方面：一方面，单一功能不断提升和多个功能的复合。如实现极化重构的频带宽度不断拓展或极化转换率不断提升、对不同方向电磁波的反射和透射相位灵活调控等。展望未来，可重构超表面还有一些新的未知功能需要我们继续挖掘。另一方面，器件和电路不断集成化、小型化，如何在有限的表面上集成更多的可重构功能？如何将控制电路和可重构超表面一体化设计？这些都值得进一步思考。此外，如何将以深度学习为代表的人工智能和可重构超表面技术有机融合，实现可重构超表面的智能感知、自主学习、自适应调节乃至智慧交互都值得深入研究。
三是基于可重构超表面的系统完善与应用。近3年，微波全息成像系统、无线信息传输系统、认知超表面系统相继研制成功。它们打破了传统的通信架构，明显简化信息传输的系统构成，使得信息传输的效率和智能化水平进一步提升。这些突破性的创新成果给可重构超表面的研究加入了助推剂。我们有理由相信运用可重构超表面可以搭建更多新架构的信息系统。未来对这一领域展开深入研究，并与工程实践相结合，必将从理论和应用两个维度推动可重构超表面向持续深入和广泛实用快速进步！
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[1] Ward K and Watts S. Use of sea clutter models in radar design and development[J]. IET Radar, Sonar & Navigation, 2010, 4(2):146-157. [2] Ward K, Tough R, and Watts S. Sea Clutter:Scattering, the K-distribution and Radar Performance, 2nd ed[M]. London:The Institution of Engineering and Technology, 2013. [3] Long M W. Radar Reflectivity of Land and Sea, 3nd ed[M]. London:Artech House radar library, 2001. [4] Skolnik M I. Radar Handbook, 3nd ed[M]. New York:The McGraw-Hill Companies Inc., 2008. [5] Gini F, Farina A, and Greco M. Selected list of references on radar signal processing[J]. IEEE Transactions on Aerospace and Electronic Systems, 2001, 37(1):329-359. [6] Daley J C, Ransone J T, Burkett J A, et al.. Sea clutter measurements on four frequencies[R]. Naval Research Laboratory Report 6806, November 1968. [7] Titi G W and Marshall D F. The ARPA/Navy Mountaintop program:adaptive signal processing for airborne early warning radar[C]. In Proceedings of 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1996:1165-1168. [8] Little M O and Berry W P. Real-time multichannel airborne radar measurements[C]. IEEE National Radar Conference, 1997:138-142. [9] Charles L R, Eckert E, Siegel A, et al.. X-band low-grazing-angle ocean backscatter obtained during LOGAN 1993[J]. IEEE Journal of Oceanic Engineering, 1997, 22(1):18-26. [10] Drosopoulos A. Description of the OHGR database[R]. Defence Research Establishment Ottawa, Technical Note 94-14, 1994. [11] Wind H J De, Cilliers J C, and Herselman P L. Sea clutter and small boat radar reflectivity databases[J]. IEEE Signal Processing Magazine, 2010, 32(2):145-148. [12] Hair T, Lee T, and Baker C J. Statistical properties of multifrequency high-range-resolution sea reflections[J]. IEE Proceedings-F, 1991, 138(2):75-79. [13] Carretero-Moya J, Gismero-Menoyo J, Blanco-del-Campo A, et al.. Statistical analysis of a high-resolution sea-clutter database[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(4):2024-2037. [14] Antipov I. Simulation of sea clutter returns[R]. Technical Report, DSTO-TR-0679, 1998. [15] Antipov I. Analysis of sea clutter data[R]. Technical Report, DSTO-TR-0647, 1998. [16] Choong P L. Modelling airborne L-band radar sea and coastal land clutter[R]. Technical Report, DSTO-TR-0945, 2000. [17] Antipov I. Statistical analysis of northern Australian coastline sea clutter data[R]. Technical Report, DSTO-TR-1236, 2002. [18] Dong Y. Clutter spatial distribution and new approaches of parameter estimation for Weibull and K-distributions[R]. Technical Report, DSTO-RR-0274, 2004. [19] Dong Y. Distribution of X-band high resolution and high grazing angle sea clutter[R]. Technical Report, DSTO-RR-0316, 2006. [20] Dong Y. High grazing angle and high resolution sea clutter correlation and polarization analyses[R]. Technical Report, DSTO-TR-1972, 2007. [21] Dong Y and Merrett D. Statistical measures of S-band sea clutter and targets[R]. Technical Report, DSTO-TR-2221, 2008. [22] Dong Y and Merrett D. Analysis of L-band multi-channel sea clutter[R]. Technical Report, DSTO-TR-2455, 2010. [23] Bocquet S. Calculation of radar probability of detection in K-distributed sea clutter and noise[R]. Technical Report, DSTO-TN-1000, 2011. [24] Weinberg G. Investigation of the Pareto distribution as a model for high grazing angle clutter[R]. Technical Report, DSTO-TR-2525, 2011. [25] Dong Y, Rosenberg L, and Weinberg G. Generating correlated gamma sequences for sea-clutter simulation[R]. Technical Report, DSTO-TR-2688, 2012. [26] Rosenberg L and Watts S. High grazing angle sea-clutter literature review[R]. Technical Report, DSTO-GD-0736, 2013. [27] Whitrow J L. A model of low grazing angle sea clutter for coherent radar performance analysis[R]. Technical Report, DSTO-TR-2864, 2013. [28] Rosenberg L. Sea-spike detection in high grazing angle X-band sea-clutter[R]. Technical Report, DSTO-TR-2820, 2013. [29] Ward K D, Barker C J, and Watts S. Maritime surveillance radar Part 1:Radar scattering from the ocean surface[J]. IEE Proceedings-F, 1990, 137(2):51-63. [30] Greco M, Gini F, and Rangaswamy M. Statistical analysis of measured polarimetric clutter data at different range resolutions[J]. IEE Proceedings-Radar, Sonar and Navigation, 2006, 153(6):473-481. [31] Gregers-Hansen V and Mital R. An improved empirical model for radar sea clutter reflectivity[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(4):3512-3524. [32] Raynal M A and Doerry A W. Doppler characteristics of sea clutter[R]. Technical Report, SAND2010-3828, 2010. [33] Watts S. Modeling and simulation of coherent sea clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(4):3303-3317. [34] Al-Ashwal W A, Woodbridge K, and Griffiths H D. Analysis of bistatic sea clutter-Part I:Average reflectivity[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2):1283-1292. [35] Al-Ashwal W A, Woodbridge K, and Griffiths H D. Analysis of bistatic sea clutter-Part Ⅱ:Amplitude statistics[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2):1293-1303. [36] Gini F and Greco M. Texture modelling, estimation and validation using measured sea clutter data[J]. IEE Proceedings-Radar, Sonar and Navigation, 2002, 149(3):115-124. [37] Plant W J. Microwave sea return at moderate to high incidence angles[J]. Waves in Random Media, 2003, 13(4):339-354. [38] Gotwols B L, Thompson D R, and Chapman R D. Ocean backscatter distribution functions at mid incidence[C]. Proceedings Engineering in Harmony with Ocean, 1993:Ⅱ/10. [39] Greco M, Stinco P, and Gini F. Impact of sea clutter nonstationarity on disturbance covariance matrix estimation and CFAR detector performance[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(3):1502-1513. [40] Lamont-Smith T, Waseda T, and Rheem C K. Measurements of the Doppler spectra of breaking waves[J]. IET Radar, Sonar & Navigation, 2007, 1(2):149-157. [41] Trunk G V and George S F. Detection of targets in non-Gaussian sea clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 1970, 6(5):620-628. [42] Trunk G V. Radar properties of non-Rayleigh sea clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 1972, 8(2):196-204. [43] Trunk G V. Non-Rayleigh sea clutter:Properties and detection of targets[R]. NRL Report, 1976, No. 7986. [44] Jakeman E and Pusey P N. A model for non-Rayleigh sea echo[J]. IEEE Transactions on Antennas and Propagation, 1976, 24(6):806-814. [45] Sangston K J and Gerlach K R. Coherent detection of radar targets in a non-Gaussian background[J]. IEEE Transactions on Aerospace and Electronic Systems, 1994, 30(2):330-340. [46] Chan H C. Radar sea-clutter at low grazing angles[J]. IEE Proceedings-F, 1990, 137(2):102-112. [47] Stehwien W. Statistics and correlation properties of high resolution X-band sea clutter[C]. Proceedings of the 1994 IEEE National Radar Conference, 1994:36-51. [48] Conte E, Maio A De, and Galdi C. Statistical analysis of real clutter at different range resolutions[J]. IEEE Transactions on Aerospace and Electronic Systems, 2004, 40(3):903-918. [49] Haykin S, Bakker R, and Currie B W. Uncovering nonlinear dynamics:The case study of sea clutter[J]. Proceedings of the IEEE, 2002, 90(5):860-881. [50] Greco M, Bordoni F, and Gini F. X-band sea-clutter nonstationarity:Influence of long waves[J]. IEEE Journal of Ocean Engineering, 2004, 29(2):269-283. [51] Watts S and Wicks D C. Empirical models for prediction in K-distribution radar sea clutter[C]. IEEE International Radar Conference, 1990:189-194. [52] Ryan J and Johnson M. Radar performance prediction for target detection at sea[C]. IEE Conference Radar-92, 1992:13-17. [53] Conte E, Lops M, and Ricci G. Asymptotically optimum radar detection in compound-Gaussian clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 1995, 31(2):617-625. [54] Conte E, Lops M, and Ricci G. Adaptive matched filter detection in spherically invariant noise[J]. IEEE Signal Processing Letters, 1996, 3(8):248-250. [55] Conte E and Maio A D. Mitigation techniques for non-Gaussian sea clutter[J]. IEEE Journal of Ocean Engineering, 2004, 29(2):284-302. [56] Pulsone N B and Raghavan R S. Analysis of an adaptive CFAR detector in non-Gaussian interference[J]. IEEE Transactions on Aerospace and Electronic Systems, 1999, 35(3):903-916. [57] Rangaswamy M. Statistical analysis of the nonhomogeneity detector for non-Gaussian interference backgrounds[J]. IEEE Transactions on Signal Processing, 2005, 53(6):2101-2111. [58] Farina A, Gini F, Greco M V, et al.. High resolution sea clutter data:Statistical analysis of recorded live data[J]. IEE Proceedings-Radar, Sonar and Navigation, 1997, 144(3):121-130. [59] Shnidman D A. Generalized radar clutter model[J]. IEEE Transactions on Aerospace and Electronic Systems, 1999, 35(3):857-865. [60] Rosenberg L and Bocquet S. Application of the Pareto plus noise distribution to medium grazing angle sea-clutter[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(1):255-261. [61] Liu Y, Frasier S J, and Mcintosh R E. Measurement and classification of low-grazing-angle radar sea spikes[J]. IEEE Transactions on Antennas and Propagation, 1998, 46(1):27-40. [62] Gutnik V G, Kulemin G P, and Sharapov L. Spike statistics features of the radar sea clutter in the millimeter wave band at extremely small grazing angles[J]. Physics and Engineering of Millimeter and Sub-Millimeter Waves, 2001, 43(3):426-428. [63] Melief H W, Greidanus H, and Genderen P. Analysis of sea spikes in radar sea clutter data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(4):985-993. [64] Lyzenga D R, Maffett A L, and Shuchman R A. The contribution of wedge scattering to the radar cross section of the ocean surface[J]. IEEE Transactions on Geoscience and Remote Sensing, 1983, 21(4):502-505. [65] Keller M R, Gotwols B L, and Chapman R D. Multiple sea spike definitions:Reducing the clutter[C]. Geoscience and Remote Sensing Symposium, 2002:940-942. [66] Posner F and Gerlach K. Sea spike demographics at high range resolutions and very low grazing angles[C]. Radar Conference, Proceedings of the 2003 IEEE, 2003:38-45. [67] Posner F L. Spiky sea clutter at high range resolutions and very low grazing angles[J]. IEEE Transactions on Aerospace and Electronic Systems, 2002, 38(1):58-73. [68] Greco M, Stinco P, and Gini F. Identification and analysis of sea radar clutter spikes[J]. IET Radar, Sonar & Navigation, 2010, 4(2):239-250. [69] Rosenberg L. Sea-spike detection in high grazing angle X-band sea-clutter[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(8):4556-4562. [70] Middleton D. New physical-statistical methods and models for clutter and reverberation:The KA-distribution and related probability structures[J]. IEEE Journal of Ocean Engineering, 1999, 24(3):261-283. [71] Ward K D and Tough R J. Radar detection performance in sea clutter with discrete spikes[C]. International Conference Radar 2002, 2002:253-257. [72] Watts S, Ward K D, and Tough R J A. The physics and modelling of discrete spikes in radar sea clutter[C]. Proceedings of International Radar Conference, 2005:72-77. [73] Rosenberg L, Crisp D J, and Stacy N J. Analysis of the KK-distribution with medium grazing angle sea-clutter[J]. IET Radar, Sonar & Navigation, 2010, 4(2):209-222. [74] Blunt S D, Gerlach K, and Heyer J. HRR detector for slow-moving targets in sea clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(3):965-974. [75] 许心瑜, 张玉石, 黎鑫, 等. L波段小擦地角海杂波KK分布建模[J]. 系统工程与电子技术, 2014, 36(7):1304-1308. Xu X Y, Zhang Y S, Li X, et al.. KK distribution modeling with L band low grazing sea clutter[J]. Systems Engineering and Electronics, 2014, 36(7):1304-1308. [76] 高彦钊, 占荣辉, 万建伟. KK分布杂波下的距离扩展目标检测算法[J]. 国防科技大学学报, 2015, 37(1):118-124. Gao Y Z, Zhan R H, and Wan J W. Range-spread target detection in KK-distributed clutter[J]. Journal of National University of Defense Technology, 2015, 37(1):118-124. [77] Ding H, Huang Y, Liu N B, et al.. Modeling of sea spike events with generalized extreme value distribution[C]. Proceedings European Radar Conference (EuRAD), 2015:113-116. [78] Balleri A, Nehorai A, and Wang J. Maximum likelihood estimation for compound-Gaussian clutter with inverse gamma texture[J]. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(2):775-780. [79] Gotwols B L and Thompson D R. Ocean microwave backscatter distributions[J]. Journal of Geophysical Research, 1994, 99(C5):9741-9750. [80] Ollila E, Tyler D E, Koivunen V, et al.. Compound-Gaussian clutter modeling with an inverse Gaussian texture distribution[J]. IEEE Signal Processing Letters, 2012, 19(12):876-879. [81] Anastassopoulos V, Lampropoulos G A, and Drosopoulos A. High resolution radar clutter statistics[J]. IEEE Transactions on Aerospace and Electronic Systems, 1999, 35(1):43-60. [82] Fayard P and Field T R. Inference of a generalised texture for a compound-Gaussian clutter[J]. IET Radar, Sonar & Navigation, 2010, 4(2):187-194. [83] Gini F, Greco M, Diani M, et al.. Performance analysis of two adaptive radar detectors against non-Gaussian real sea clutter data[J]. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(4):1429-1439. [84] Farshchian M and Posner F L. The Pareto distribution for low grazing angle and high resolution X-band sea clutter[C]. IEEE Radar Conference, 2010:789-793. [85] Rosenberg L and Bocquet S. The Pareto distribution for high grazing angle sea-clutter[C]. IEEE International Geoscience and Remote Sensing Symposium, 2013:4209-4212. [86] Weinberg G V. Assessing Pareto fit to high-resolution high-grazing-angle sea clutter[J]. Electronics Letters, 2011, 47(8):516-517. [87] Rosenberg L, Watts S, and Bocquet S. Application of the K+Rayleigh distribution to high grazing angle sea-clutter[C]. International Radar Conference, 2014:1-6. [88] Fiche A, Angelliaume S, Rosenberg L, et al.. Analysis of X-Band SAR sea-clutter distributions at different grazing angles[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(8):4650-4660. [89] Hu J, Tung W, and Gao J. A new way to model nonstationary sea clutter[J]. IEEE Signal Processing Letters, 2009, 16(2):129-132. [90] Tsihrintzis G A and Nikias C L. Evaluation of fractional, lower-order statistics-based detection algorithms on real radar sea-clutter data[J]. IEE Proceedings-Radar, Sonar and Navigation, 1997, 144(1):29-37. [91] 石志广, 周剑雄, 付强. K分布海杂波参数估计方法研究[J]. 信号处理, 2007, 23(3):420-424. Shi Z G, Zhou J X, and Fu Q. Parameter estimation study of K-distributed sea clutter[J]. Signal Processing, 2007, 23(3):420-424. [92] Marier L J. Correlated K-distributed clutter generation for radar detection and track[J]. IEEE Transactions on Aerospace and Electronic Systems, 1995, 31(2):568-580. [93] Davidson G. Simulation of coherent sea clutter[J]. IET Radar, Sonar & Navigation, 2010, 4(2):168-177. [94] Pidgeon V W. Doppler dependence of radar sea return[J]. Journal of Geophysical Research, 1968, 73:1333-1341. [95] Lee P, Barter J D, Beach K L, et al.. Power spectral lineshapes of microwave radiation backscattered from sea surfaces at small grazing angle[J]. IEE Proceedings-Radar, Sonar and Navigation, 1995, 142(5):252-258. [96] Lamont-Smith T. An empirical model of EM scattering from steepening wave profiles derived from numerical computations[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(6):1447-1454. [97] Lamont-Smith T. Investigation of the variability of Doppler spectra with radar frequency and grazing angle[J]. IEE Proceedings-Radar, Sonar and Navigation, 2004, 151(5):291-298. [98] Lamont-Smith T. Azimuth dependence of Doppler spectra of sea clutter at low grazing angle[J]. IET Radar, Sonar & Navigation, 2008, 2(2):97-103. [99] Lamont-Smith T, Mitomi M, Kawamura T, et al.. Electromagnetic scattering from wind blown waves and ripples modulated by longer waves under laboratory conditions[J]. IET Radar, Sonar & Navigation, 2010, 4(2):265-279. [100] Lee P, Barter J D, and Lake B M. Lineshape analysis of breaking wave Doppler spectra[J]. IEE Proceedings-Radar, Sonar and Navigation, 1998, 145(2):135-139. [101] Walker D. Experimentally motivated model for low grazing angle radar Doppler spectra of the sea surface[J]. IEE Proceedings-Radar, Sonar and Navigation, 2000, 147(3):114-120. [102] Walker D. Doppler modelling of radar sea clutter[J]. IEE Proceedings-Radar, Sonar and Navigation, 2001, 148(2):73-80. [103] Rosenberg L. Characterization of high grazing angle X-band sea-clutter Doppler spectra[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(1):406-417. [104] Rosenberg L and Stacy N J. Analysis of medium grazing angle X-band sea-clutter Doppler spectra[C]. Proceedings of the IEEE Radarcon Conference, 2008:1-6. [105] Watts S. The effects of covariance matrix mismatch on adaptive CFAR performance[C]. IEEE Radar 2013 International Conference, 2013:495-499. [106] Miller R J. Variability in spectra of low-grazing angle sea clutter returns[R]. NATO/RTO Publications, Proceedings of SET Symposium on Low Grazing Angle Clutter:Its Characterisation, Measurement and Application, 2000. [107] Ritchie M A, Stove A G, Watts S, et al.. Application of a new sea clutter Doppler model[C]. IEEE Radar 2013 International Conference, 2013:560-565. [108] Watts S, Rosenberg L, Bocquet S, et al.. Doppler spectra of medium grazing angle sea clutter Part 1:Characterisation[J]. IET Radar, Sonar & Navigation, 2016, 10(1):24-31. [109] Watts S, Rosenberg L, Bocquet S, et al.. Doppler spectra of medium grazing angle sea clutter Part 2:Model assessment and simulation[J]. IET Radar, Sonar & Navigation, 2016, 10(1):32-42. [110] Baker C J. K-distributed coherent sea clutter[J]. IEE Proceedings-F, 1991, 138(2):89-92. [111] Ritchie M A, Woodbridge K, and Stove A G. Analysis of sea clutter distribution variation with Doppler using the compound K-distribution[C]. IEEE Radar 2010 International Conference, 2010:495-499. [112] Ding H, Sun Y L, Liu N B, et al.. Bispectrum property analysis of high resolution real sea clutter[C]. IET International Radar Conference, 2015:1-4. [113] Haykin S and Thomson D J. Signal detection in a nonstationary environment reformulated as an adaptive pattern classification problem[J]. Proceedings of the IEEE, 1998, 86(11):2325-2344. [114] Guan J, Zhang J, and Liu N B. Time-frequency entropy of Hilbert-Huang transformation for detecting weak target in the sea clutter[C]. 2009 IEEE Radar Conference, 2009:1-5. [115] 张建, 黄勇, 关键, 等. 基于局部Hilbert边际谱隶属度的微弱目标检测算法[J]. 信号处理, 2011, 27(9):1335-1340. Zhang J, Huang Y, Guan J, et al.. Weak target detection based on the membership degree of partial Hilbert marginal spectrum[J]. Signal Processing, 2011, 27(9):1335-1340. [116] Chen X L, Guan J, Bao Z H, et al.. Detection and extraction of target with micro-motion in spiky sea clutter via short-time fractional Fourier transform[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(2):1002-1018. [117] Rosenberg L. The effect of temporal correlation with K and KK-distributed sea-clutter[C]. IEEE Radarcon Conference, 2012:0303-0308. [118] Siegel A, Ochadlick A, Davis Jr J, et al.. Spatial and temporal correlation of LOGAN-1 high-resolution radar sea clutter data[C]. IEEE International Conference on Geoscience and Remote Sensing Symposium, 1994:818-821. [119] Farina A, Gini F, Greco M V, et al.. Improvement factor for real sea-clutter Doppler frequency spectra[J]. IEE Proceedings-Radar, Sonar and Navigation, 1996, 143(5):341-344. [120] Watts S. Cell-averaging CFAR gain in spatially correlated K-distributed clutter[J]. IEE Proceedings-Radar, Sonar and Navigation, 1996, 143(5):321-327. [121] Raghavan R S. A model for spatially correlated radar clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 1991, 27(2):268-275. [122] Lombardo P and Oliver C J. Estimating the correlation properties of K-distributed SAR clutter[J]. IEE Proceedings-Radar, Sonar and Navigation, 1995, 142(4):167-178. [123] Watts S and Ward K D. Spatial correlation in K-distributed sea clutter[J]. IEE Proceedings F Communications, Radar and Signal Processing, 1987, 134(6):526-532. [124] Armstrong B C and Griffiths H D. Modeling spatially correlated K-distributed clutter[J]. Electronics Letters, 1991, 27(15):1355-1356. [125] Armstrong B C and Griffiths H D. CFAR detection of fluctuating targets in spatially correlated K-distributed clutter[J]. IEE Proceedings F-Radar and Signal Processing, 1991, 138(2):139-152. [126] Tough R J A, Ward K D, and Shepherd P W. The modelling and exploitation of spatial correlation in spiky sea clutter[C]. EMRS DTC 2nd Annual Conference, 2005:A1. [127] 关键, 丁昊, 黄勇, 等. 实测海杂波数据空间相关性研究[J]. 电波科学学报, 2012, 27(5):943-954. Guan J, Ding H, Huang Y, et al.. Spatial correlation property with measured sea clutter data[J]. Chinese Journal of Radio Science, 2012, 27(5):943-954. [128] Ding H, Guan J, Liu N B, et al.. New spatial correlation models for sea clutter[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(9):1833-1837. [129] 何友, 黄勇, 关键, 等. 海杂波中的雷达目标检测技术综述[J]. 现代雷达, 2014, 36(12):1-9. He Y, Huang Y, Guan J, et al.. An overview on radar target detection in sea clutter[J]. Modern Radar, 2014, 36(12):1-9. [130] Stankovic L, Thayaparan T, and Dakovic M. Signal decomposition by using the S-method with application to the analysis of HF radar signals in sea-clutter[J]. IEEE Transactions on Signal Processing, 2006, 54(11):4332-4342. [131] Dong Y H and Crisp D J. The Euler decomposition and its application to sea clutter analysis[C]. IEEE International Conference on Radar, 2008:133-138. [132] Huang N E, Shen Z, Long S R, et al.. The empirical mode decomposition and the Hilbert spectrum for nonlinear and Non-stationary time series analysis[J]. Proceedings of the Royal Society A:Mathematical, Physical and Engineering Science, 1998, 454(1971). doi: 10.1098/rspa.1998.0193. [133] Gini F and Greco M. Texture modelling, estimation and validation using measured sea clutter data[J]. IEE Proceedings-Radar, Sonar and Navigation, 2002, 149(3):115-124. [134] Gini F, Giannakis G B, Greco M, et al.. Time-averaged subspace methods for radar clutter texture retrieval[J]. IEEE Transactions on Signal Processing, 2001, 49(9):1886-1898. [135] Dinesh R and Jeffrey K. Adaptive radar detection in doubly nonstationary autoregressive Doppler spread clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2009, 45(2):484-497. [136] Carretero-Moya J, Gismero-Menoyo J, Asensio-Lopez A, et al.. Small-target detection in high-resolution heterogeneous sea-clutter:an empirical analysis[J]. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(3):1880-1898. [137] Leung H and Lo T. Chaotic radar signal processing over the sea[J]. IEEE Journal of Ocean Engineering, 1993, 18(3):287-295. [138] Unsworth C P, Cowper M R, Mclaughlin S, et al.. Re-examining the nature of radar sea clutter[J]. IEE Proceedings-Radar, Sonar and Navigation, 2002, 149(3):105-114. [139] Mcdonald M and Damini A. Limitations of nonlinear chaotic dynamics in predicting sea clutter returns[J]. IEE Proceedings-Radar, Sonar and Navigation, 2004, 151(2):105-113. [140] Blu T and Unser M. Self-similarity:Part Ⅱ-Optimal estimation of fractal processes[J]. IEEE Transactions on Signal Processing, 2007, 55(4):1364-1378. [141] Hu J, Tung W W, and Gao J B. Detection of low observable targets within sea clutter by structure function based multifractal analysis[J]. IEEE Transactions on Antenna and Propagation, 2006, 54(1):136-143. [142] Hu J, Gao J B, Posner F L, et al.. Target detection within sea clutter:A comparative study by fractal scaling analyses[J]. Fractals, 2006, 14(3):187-204. [143] Peng C K, Buldyrev S V, Goldberger A L, et al.. Statistical properties of DNA sequences[J]. Physica A, 1995, 221(1/3):180-192. [144] Kantelhardt J W, Koscielny-Bunde E, Rego H H A, et al.. Detecting long range correlations with detrended fluctuation analysis[J]. Physica A, 2001, 295(3/4):441-454. [145] Bashan A, Bartsch R, Kantelhardt J W, et al.. Comparison of detrending methods for fluctuation analysis[J]. Physica A, 2008, 387(21):5080-5090. [146] Xu X K. Low observable targets detection by joint fractal properties of sea clutter:An experimental study of IPIX OHGR datasets[J]. IEEE Transactions on Antennas and Propagation, 2010, 58(4):1425-1429. [147] 许小可. 基于非线性分析的海杂波处理与目标检测[D].[博士论文], 大连海事大学, 2008. Xu X K. Sea clutter processing and target detecting based on nonlinear analysis[D].[Ph.D. dissertation], Dalian Maritime University, 2008. [148] 丁昊, 关键, 黄勇, 等. 非平稳海杂波的消除趋势波动分析[J]. 电波科学学报, 2013, 28(1):116-123. Ding H, Guan J, Huang Y, et al.. Detrended fluctuation analysis of non-stationary sea clutter[J]. Chinese Journal of Radio Science, 2013, 28(1):116-123. [149] Ding H, Wang G Q, and Guan J. Analysis of sea clutter fractal property and target detection based on fit error[C]. Asia-Pacific Conference on Antennas and Propagation Conference, 2012. [150] Guan J, Liu N B, Zhang J, et al.. Multifractal correlation characteristic for radar detecting low-observable target in sea clutter[J]. Signal Processing, 2010, 90(2):523-535. [151] Liu N B, Ding H, Xue Y H, et al.. Approximate fractality of sea clutter fractional Fourier transform spectrum[C]. Proceedings European Radar Conference (EuRAD), 2015:117-120. [152] Guan J, Liu N B, Huang Y, et al.. Fractal characteristic in frequency domain for target detection within sea clutter[J]. IET Radar, Sonar & Navigation, 2012, 6(5):293-306. [153] 刘宁波, 关键, 黄勇, 等. 基于海杂波频谱多尺度Hurst指数的目标检测方法[J]. 电子学报, 2013, 41(3):424-431. Liu N B, Guan J, Huang Y, et al.. Target detection within sea clutter based on multi-scale Hurst exponent in frequency domain[J]. Acta Electronica Sinica, 2013, 41(3):424-431. [154] 刘宁波, 关键, 王国庆, 等. 基于海杂波FRFT谱多尺度Hurst指数的目标检测方法[J]. 电子学报, 2013, 41(9):1847-1853. Liu N B, Guan J, Wang G Q, et al.. Target detection within sea clutter based on multi-scale Hurst exponent in FRFT domain[J]. Acta Electronica Sinica, 2013, 41(9):1847-1853. [155] 刘宁波, 黄勇, 关键, 等. 实测海杂波频域分形特性分析[J]. 电子与信息学报, 2012, 34(4):929-935. Liu N B, Huang Y, Guan J, et al.. Fractal analysis of real sea clutter in frequency domain[J]. Journal of Electronics & Information Technology, 2012, 34(4):929-935. -
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