雷达回波信号建模与仿真研究
学习matlab以及雷达信号处理的资料，关于雷达信号处理的matlab代码以及指导。ABSTRACTThe Modeling and Simulation of radar signal plays an important role in the design ofmodem radar system. This paper analyses the statistical model of target echo and clutter receivedy radar.in the simulation for target echo, First we analyse the mathematical expression for radartarget echo on the base ofradar equation. Second, presenting three main calculational methods ofthe deterministic model of Radar Cross Section(RCS), and introducing the calculational formulafor some simple object, some average RCS data of familiar objects in microwave bandMeanwhile, the statistical model of RCs - Swerling model are studied and the method toproduce the Swerling sequences be given. Third, two algorithms for producing unifomdistribution are analysed thoroughly by us-mid-square method and linear congruential methodThrough the comparision of the performance of the two algorithms, we select the latter toproduce the uniform distribution sequence in (0, 1). Fourth, presenting the primary principle ofline array and simulating the target echo received by ULA, Fifth, we derive the mathematicalquation of the instantaneous DOA, when the target moving in line and in the same plane asULAThis paper simulates the correlated-coherent radar clutter model. First, presenting the basictheory of zero memoriless nonlinear method and sphere invariable random process method,thedifferent fields where the four amplitude distributions(rayleigh, log-normal, weibull, k)can beapplied in. Second, analyzing three algorithms to design shaping filter AR model, minimunphase. frequency sampling. After comparing the correlation performance of sequences which aregenerated in these algorithms separately, we decided to use frequency sampling method has thebest performance to design shaping filter. Third, this paper analyse clutter models which submitGauss power spectrum and forenamed four amplitude distributions. Meanwhile, we introduce thenoncentral chi-square gamma distribution that can interpret the physical reason of radar clutterbetter. Fourth, we simulate different clutter models, the result is useful for practical workKey words: target echo; clutter; shaping filter; ZMNL; SIRP第I页信息T程大学硕十学位论文表目录表1几种简单形状几何目标的RCS计算式表2微波波段常见目标统计平均RCS数据表3不同条件下韦布尔分布的形状参数第V页信息工程大学硕士学位论文图目录图1舍取法示意图图2舍取法与理论值的比较,图3平方取中法所得序列4省垂10图4乘同余法所得序列10图5平方取中法所得序列4.·■·『▲晶图6乘同余法所得序列,,。。。,11图7线阵列天线示意图●命12图8相位扫描原理13图9距离参考点z的阵元接收单目标平面波信号.14图10阵列信号模型图11回波信号波形16图12MsIC算法估计空间谱16图13回波信号波形16图14 MUSIC算法估计空间谱16图153个目标信号示意图.17图163个目标的MSTC算法估计空间谱17图17目标在阵列平面内做直线运动,18图18ZMAN法产生杂波原理图.23图19SIRP法产生杂波原理图bD画24图20成形滤波器工作模型24图21相关杂波波形图22杂波概率密度函数身●25图23杂波功率谱密度图24成形滤波器示意图图25三种不同方法的相关系数比较568图26三种不同方法的相关系数比较bp非9图27三种不同方法的相关系数比较29图28相于瑞利分布杂波产生模型图29相于杂波实部图30相干杂波虚部000图31杂波概率密度函数.31图32杂波功率谱密度31第页信息工程大学硕于学位论文图33相关对数正态分布杂波产生模型31图34相关杂波波形.32图35杂波概率密度函数32图36杂波功率谱密度。32图37相干对数正态分布杂波改进模型图38相干杂波实部35图39相于杂波虚部35图40杂波概率密度函数35图41杂波功率谱密度35图42相关韦布尔分布杂波产生模型36图43高斯超几何函数理论与实际值的比较图44应用变换公式后理论与实际值的比较A图45不同阶次时渐近曲线的比较37图46不同阶次的高斯超几何函数理论与实际值的比较37图47相关杂波波形∴鲁會·非..38图48杂波概率密度函数曾·◆·↓鲁·参◆伊●38图49杂波功率谱密度,,,,,,38图50相干韦布尔分布杂波产生模型38图51相干杂波实部41图52相干杂波虚部图53杂波概率密度函数鲁·看参·命鲁鲁PD鲁命會曹會请鲁■P鲁41图54杂波功率谱密度,,,图55不同N时NG的概率密度函数■■·D43图56三种分布概率密度函数的比较.43图57NG分布杂波产生模型图58杂波波形图59杂波概率密度函数45图60杂波功率谱密度图61相关K分布杂波产生模型图62相关杂波波形图63杂波概率密度函数·◆···曾·47图64杂波功率谱密度,47图65相干K分布杂波产生模型48图66相于杂波实部第Ⅴ页信息了程大学硕土学位论文图67相干杂波虚部9图68杂波概率密度函数49图69杂波功率谱密度…,。,,。。49第VI萸独创性声明所提交的学位论文是本人在导师指导下进行的研究工作及取得的研究成果。尽我所知,除了文中标注和致谢的相关内容外,论文中不包含其他个人或集体已经公开的研究成果。与我一同工作的同志对本研究所做的任何贡献均已在论文中作了明确的说明并表示谢意学位论文题目:罾达回信建模研沿学位论文作者签各:剑奇日期:乙年月26学位论文版权使用授权书本人完全了解信息工程大学有关保留、使用学位论文的规定。本人授权信息工程大学可以保留并向国家有关部门或机构送交论文的复印件和电子文档,允许论文被查阅和借阅;可以将学位论文的全部或部分内容编入有关数据库进行检索,可以采用影印、缩印或扫描等复制手段保存、汇编学位论文。(涉密学位论文在解密后适用本授权书。)学位论文题H雷达回信建模仿研强去学位论文作者签名日期:20年白月26日你者指导教师名二大种日期206年6月6日信息「程人学硕士学位论文第一章绪论1.1课题的背景及意义现代雷达系统日益变得复杂,难以用简单直观的分析方法进行处理,这就促进了霞达仿真方法的推广。雷达系统仿真就是用计算机软件来建立雷达系统的模型,利用数字计算机技术来逼真地复现雷达工作的动态过程,它是计算机技术、数字模拟技术和雷达技术相结合的产物。具体地说,仿真的对象是雷达系统,它包括雷达本身、雷达目标及目标环境仿真的手段是计算机和数字信号处理技术;仿真的方式和目的就是为雷达系统建模,并尽可能真实地复现蕴含雷达系统动态工作模式、雷达目标及目标坏境信息的雷达信号。这里所讲的“复现”就是重现雷达信号的产生、传递、处理等动态过程。从时间关系上看,就是重现一个随机的时间序列。目标国波信号与目标形状、截面积RCS和天线波束的照射方向等有密切的关系。在杂波背景下检测出雷达目标也是雷达信号处理的一个重要课题,杂波模型的统计特性直接影响雷达最住检测器的设计。对杂波的建模与仿真是利用雷达最佳检测理论来设计最佳检测器结构的重要手段本文的目的是总结雷达接收目标回波、杂波的数学模型,针对目标回波信号和杂波信号进行仿真研究,模拟出幅度服从一定分布并同时具有一定功率谱的杂波信号序列。在没有雷达系统前端的情况下,对系统后级信号处理部分进行调试和测试,为现代雷达系统的设计、分析和效能评估提供了理论依据1.2国内外研究现状雷达的作用距离、分辨能力、测量精度和单值性等性能指标对信号处理技术提出越来越高的要求。在实现最佳处理并保证一定信噪比的前提下,测量精度和分辨力对信号形式的要求是一致的。我们通常根据不同的情况,结合雷达截面积RCS及其起伏特性,对目标信号进行建模仿真M&S)真实的世界是错综复杂的,要为其建立精确的数学模型是一件极其困难的事情,而建立的这些数学模型通常是近似片面的,对雷达杂波的研究过程也是如此。雷达杂波仿真技术在几十年的发展过程中,经过了一个由浅到深、由简到繁、由粗到细的过程。目前使用的杂波模型主要有三种方式:(1)描述杂波幅度和功率谱的统计模型;(2)描述杂波散射单元机理的机理模型;③)描述由试验数据拟和δ与频率、极化、俯角、环境参数等物理量之间依赖关系的关系模型。从二十世纪七十年代以来,人们一直致力于雷达统计模型的研究在早期的工作中,认为杂波是~种高斯噪声,为杂波提供了一种结果非常简单的模型。后来通过对窄脉冲雷达的测量发现用高斯分布来描述杂波是不够的,杂波的分布函数表现出个较长的拖尾,明显长于高斯分布模型。因此,在八十年代,人们又提出了对数正态分布和韦布尔分布来拟合数据。随着研究的深入,一种半实验化模型K分布被提出,并逐渐第1页信息丁程大学硕士学位论文成熟起来。在很多情况下,我们把雷达杂波模型看成一个实随机过程。例如,在相干检测时,只保留杂波的同相分量,而丢弃正交分量。然而,雷达的最佳检测是不应该丢弃正交分量的。因为除非在高斯背景下,否则正交分量是同判决有关的。由于在有信号时的信号幅度的概率密度是依赖于杂波的幅度和相位的,如果把杂波当成一个非相干的实过程,就不能满足这个要求。近年来,相干相关杂波的建模仿真引起了人们的广泛关注。目前国内外对杂波的仿真方法主要有零记忆非线性变化法MNL)、球不变随机过程法(SIP和随机微分方程法SDE)ZMNL法可以实现用于描述雷达杂波的几种常用分布的仿真,它易于实现且产生相关雷达杂波序列的速度较快,是目前仿真中最引人关注的方法,但其应用受到功率谱形状等因素的制约。SRP模型属于外生模型,能够独立控制序列的概率密度函数和自相关函数。在相关雷达杂波仿真中,可以用SRP法仿真相关瑞利、韦布尔和K分布杂波。由于受所需仿真序列的阶数及自相关函数的限制,当所需仿真序列较长时,计算负荷很大,不易形成快速算法。SRP法在国内受到的关注不多,基本上停留在基本原理的理解上。SDE法没有SRP法和ZMNL法流行,在国内少有人提及,国外对这方面的研究也不多,且主要用于对通信系统中于扰信号的仿真。根据SDE的理论,它可以通过相关时间来控制序列的相关性1,同样适用于相关雷达杂波的仿真。SDE法实际上是个非线性自回归模型,具有产生速度相当快的优点,但这种方法对概率密度函数有一定的限制。国内在杂波研究方面的某些应用已经进入实用阶段,杂波的模拟已经被运用在内场仿真系统中。另外,有些科研单位也在砑究从实际地图产生真实杂波的仿真方法。1.3论文结构和主要内容本文针对“空间目标搜索截获跟踪能力仿真分析”项目的要求,对雷达目标回波信号和杂波信号的幅度分布、功率谱分布模型进行了分析,在零记忆非线性变换法和球不变随机过程法的基础上对非相于相关和相干相关杂波信号迸行了仿真。本文的具体结构如下第一章“绪论”,总结了雷达系统建模与仿真的发展、特点及其应用,介绍了本课题的来源及主要研究内容,并给出了本文的主要工作和结构第二章“目标回波信号模拟”,在比较产生(0,1)均匀分随机序列的平方取中法和线性同余法后,采用线性同余法的特例一乘同余法,产生了(0,1)均匀分布随机序列;分析了描述雷达截面积起伏的四种 Swerling模型,并给出了产生与之对应的信号幅度的方法。对目标的波达方向固定不变以及目标在线阵平面内做直线运动时的瞬时波达方向表达式进行分析,并结合雷达方程对等距线阵接收目标回波信号进行了仿真。第三章“杂波建模仿真”,对雷达杂波统计模型迸行了深入分析,通过比较设计成形滤波器的三种方法(AR模型法、最小相位法、频率抽样法)生成序列的相关特性,采用频率第2页
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凸优化在信号处理与通信中的应用Convex Optimization in Signal Processing and Communications
凸优化理论在信号处理以及通信系统中的应用 比较经典的通信系统凸优化入门教程ContentsList of contributorspage IxPrefaceAutomatic code generation for real- time convex optimizationJacob Mattingley and stephen Boyd1.1 Introduction1.2 Solvers and specification languages61. 3 Examples121. 4 Algorithm considerations1.5 Code generation261.6 CVXMOD: a preliminary implementation281.7 Numerical examples291. 8 Summary, conclusions, and implicationsAcknowledgments35ReferencesGradient-based algorithms with applications to signal-recoveryproblemsAmir beck and marc teboulle2.1 Introduction422.2 The general optimization model432.3 Building gradient-based schemes462. 4 Convergence results for the proximal-gradient method2.5 A fast proximal-gradient method2.6 Algorithms for l1-based regularization problems672.7 TV-based restoration problems2. 8 The source-localization problem772.9 Bibliographic notes83References85ContentsGraphical models of autoregressive processes89Jitkomut Songsiri, Joachim Dahl, and Lieven Vandenberghe3.1 Introduction893.2 Autoregressive processes923.3 Autoregressive graphical models983. 4 Numerical examples1043.5 Conclusion113Acknowledgments114References114SDP relaxation of homogeneous quadratic optimization: approximationbounds and applicationsZhi-Quan Luo and Tsung-Hui Chang4.1 Introduction1174.2 Nonconvex QCQPs and sDP relaxation1184.3 SDP relaxation for separable homogeneous QCQPs1234.4 SDP relaxation for maximization homogeneous QCQPs1374.5 SDP relaxation for fractional QCQPs1434.6 More applications of SDP relaxation1564.7 Summary and discussion161Acknowledgments162References162Probabilistic analysis of semidefinite relaxation detectors for multiple-input,multiple-output systems166Anthony Man-Cho So and Yinyu Ye5.1 Introduction1665.2 Problem formulation1695.3 Analysis of the SDr detector for the MPsK constellations1725.4 Extension to the Qam constellations1795.5 Concluding remarks182Acknowledgments182References189Semidefinite programming matrix decomposition, and radar code design192Yongwei Huang, Antonio De Maio, and Shuzhong Zhang6.1 Introduction and notation1926.2 Matrix rank-1 decomposition1946.3 Semidefinite programming2006.4 Quadratically constrained quadratic programming andts sdp relaxation201Contents6.5 Polynomially solvable QCQP problems2036.6 The radar code-design problem2086.7 Performance measures for code design2116.8 Optimal code design2146.9 Performance analysis2186.10 Conclusions223References226Convex analysis for non-negative blind source separation withapplication in imaging22Wing-Kin Ma, Tsung-Han Chan, Chong-Yung Chi, and Yue Wang7.1 Introduction2297.2 Problem statement2317.3 Review of some concepts in convex analysis2367.4 Non-negative, blind source-Separation criterion via CAMNS2387.5 Systematic linear-programming method for CAMNS2457.6 Alternating volume-maximization heuristics for CAMNS2487.7 Numerical results2527.8 Summary and discussion257Acknowledgments263References263Optimization techniques in modern sampling theory266Tomer Michaeli and yonina c. eldar8.1 Introduction2668.2 Notation and mathematical preliminaries2688.3 Sampling and reconstruction setup2708.4 Optimization methods2788.5 Subspace priors2808.6 Smoothness priors2908.7 Comparison of the various scenarios3008.8 Sampling with noise3028. 9 Conclusions310Acknowledgments311References311Robust broadband adaptive beamforming using convex optimizationMichael Rubsamen, Amr El-Keyi, Alex B Gershman, and Thia Kirubarajan9.1 Introduction3159.2 Background3179.3 Robust broadband beamformers3219.4 Simulations330Contents9.5 Conclusions337Acknowledgments337References337Cooperative distributed multi-agent optimization340Angelia Nedic and asuman ozdaglar10.1 Introduction and motivation34010.2 Distributed-optimization methods using dual decomposition34310.3 Distributed-optimization methods using consensus algorithms35810.4 Extensions37210.5 Future work37810.6 Conclusions38010.7 Problems381References384Competitive optimization of cognitive radio MIMO systems via game theory387Gesualso Scutari, Daniel P Palomar, and Sergio Barbarossa11.1 Introduction and motivation38711.2 Strategic non-cooperative games: basic solution concepts and algorithms 39311.3 Opportunistic communications over unlicensed bands411.4 Opportunistic communications under individual-interferenceconstraints4151.5 Opportunistic communications under global-interference constraints43111.6 Conclusions438Ackgment439References43912Nash equilibria: the variational approach443Francisco Facchinei and Jong-Shi Pang12.1 Introduction44312.2 The Nash-equilibrium problem4412. 3 EXI45512.4 Uniqueness theory46612.5 Sensitivity analysis47212.6 Iterative algorithms47812.7 A communication game483Acknowledgments490References491Afterword494Index49ContributorsSergio BarbarossaYonina c, eldarUniversity of rome-La SapienzaTechnion-Israel Institute of TechnologyHaifaIsraelAmir beckTechnion-Israel instituteAmr El-Keyiof TechnologyAlexandra universityHaifEgyptIsraelFrancisco facchiniStephen boydUniversity of rome La sapienzaStanford UniversityRomeCaliforniaItalyUSAAlex b, gershmanTsung-Han ChanDarmstadt University of TechnologyNational Tsing Hua UniversityDarmstadtHsinchuGermanyTaiwanYongwei HuangTsung-Hui ChangHong Kong university of scienceNational Tsing Hua Universityand TechnologyHsinchuHong KongTaiwanThia KirubarajanChong-Yung chiMcMaster UniversityNational Tsing Hua UniversityHamilton ontarioHsinchuCanadaTaiwanZhi-Quan LuoJoachim dahlUniversity of minnesotaanybody Technology A/sMinneapolisDenmarkUSAList of contributorsWing-Kin MaMichael rebsamenChinese University of Hong KongDarmstadt UniversityHong KonTechnologyDarmstadtAntonio de maioGermanyUniversita degli studi di napoliFederico iiGesualdo scutariNaplesHong Kong University of Sciencealyand TechnologyHong KongJacob MattingleyAnthony Man-Cho SoStanford UniversityChinese University of Hong KongCaliforniaHong KongUSAJitkomut songsinTomer michaeliUniversity of californiaTechnion-Israel instituteLoS Angeles. CaliforniaogyUSAHaifaMarc teboulleTel-Aviv UniversityAngelia NedicTel-AvUniversity of Illinois atIsraelUrbana-ChampaignInoSLieven VandenbergheUSAUniversity of CaliforniaLos Angeles, CaliforniaUSAAsuman OzdaglarMassachusetts Institute of TechnologyYue WangBoston massachusettsVirginia Polytechnic InstituteUSAand State UniversityArlingtonDaniel p palomarUSAHong Kong University ofScience and TechnologyYinyu YeHong KongStanford UniversityCaliforniaong-Shi PangUSAUniversity of illinoisat Urbana-ChampaignShuzhong zhangIllinoisChinese university of Hong KongUSAHong KongPrefaceThe past two decades have witnessed the onset of a surge of research in optimization.This includes theoretical aspects, as well as algorithmic developments such as generalizations of interior-point methods to a rich class of convex-optimization problemsThe development of general-purpose software tools together with insight generated bythe underlying theory have substantially enlarged the set of engineering-design problemsthat can be reliably solved in an efficient manner. The engineering community has greatlybenefited from these recent advances to the point where convex optimization has nowemerged as a major signal-processing technique on the other hand, innovative applica-tions of convex optimization in signal processing combined with the need for robust andefficient methods that can operate in real time have motivated the optimization commu-nity to develop additional needed results and methods. The combined efforts in both theoptimization and signal-processing communities have led to technical breakthroughs ina wide variety of topics due to the use of convex optimization This includes solutions tonumerous problems previously considered intractable; recognizing and solving convex-optimization problems that arise in applications of interest; utilizing the theory of convexoptimization to characterize and gain insight into the optimal-solution structure and toderive performance bounds; formulating convex relaxations of difficult problems; anddeveloping general purpose or application-driven specific algorithms, including thosethat enable large-scale optimization by exploiting the problem structureThis book aims at providing the reader with a series of tutorials on a wide varietyof convex-optimization applications in signal processing and communications, writtenby worldwide leading experts, and contributing to the diffusion of these new developments within the signal-processing community. The goal is to introduce convexoptimization to a broad signal-processing community, provide insights into how convexoptimization can be used in a variety of different contexts, and showcase some notablesuccesses. The topics included are automatic code generation for real-time solvers, graphical models for autoregressive processes, gradient-based algorithms for signal-recoveryapplications, semidefinite programming(SDP)relaxation with worst-case approximationperformance, radar waveform design via SDP, blind non-negative source separation forimage processing, modern sampling theory, robust broadband beamforming techniquesdistributed multiagent optimization for networked systems, cognitive radio systems viagame theory, and the variational-inequality approach for Nash-equilibrium solutionsPrefaceThere are excellent textbooks that introduce nonlinear and convex optimization, providing the reader with all the basics on convex analysis, reformulation of optimizationproblems, algorithms, and a number of insightful engineering applications. This book istargeted at advanced graduate students, or advanced researchers that are already familiarwith the basics of convex optimization. It can be used as a textbook for an advanced graduate course emphasizing applications, or as a complement to an introductory textbookthat provides up-to-date applications in engineering. It can also be used for self-study tobecome acquainted with the state of-the-art in a wide variety of engineering topicsThis book contains 12 diverse chapters written by recognized leading experts worldwide, covering a large variety of topics. Due to the diverse nature of the book chaptersit is not possible to organize the book into thematic areas and each chapter should betreated independently of the others. a brief account of each chapter is given nextIn Chapter 1, Mattingley and Boyd elaborate on the concept of convex optimizationin real-time embedded systems and automatic code generation. As opposed to genericsolvers that work for general classes of problems, in real-time embedded optimization thesame optimization problem is solved many times, with different data, often with a hardreal-time deadline. Within this setup the authors propose an automatic code-generationsystem that can then be compiled to yield an extremely efficient custom solver for theproblem familyIn Chapter 2, Beck and Teboulle provide a unified view of gradient-based algorithmsfor possibly nonconvex and non-differentiable problems, with applications to signalrecovery. They start by rederiving the gradient method from several different perspectives and suggest a modification that overcomes the slow convergence of the algorithmThey then apply the developed framework to different image-processing problems suchas e1-based regularization, TV-based denoising, and Tv-based deblurring, as well ascommunication applications like source localizationIn Chapter 3, Songsiri, Dahl, and Vandenberghe consider graphical models for autore-gressive processes. They take a parametric approach for maximum-likelihood andmaximum-entropy estimation of autoregressive models with conditional independenceconstraints, which translates into a sparsity pattern on the inverse of the spectral-densitymatrix. These constraints turn out to be nonconvex. To treat them the authors proposea relaxation which in some cases is an exact reformulation of the original problem. Theproposed methodology allows the selection of graphical models by fitting autoregressiveprocesses to different topologies and is illustrated in different applicationsThe following three chapters deal with optimization problems closely related to SDPand relaxation techniquesIn Chapter 4, Luo and Chang consider the SDP relaxation for several classes ofquadratic-optimization problems such as separable quadratically constrained quadraticprograms(QCQPs)and fractional QCQPs, with applications in communications and signal processing. They identify cases for which the relaxation is tight as well as classes ofquadratic-optimization problems whose relaxation provides a guaranteed, finite worstcase approximation performance. Numerical simulations are carried out to assess theefficacy of the SDP-relaxation approach
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