77GHz多载频MIMO汽车雷达信号处理方法的研究
77GHz多载频MIMO汽车雷达信号处理方法的研究，主要是一分期刊感觉值1分微波学报2017年第33卷增刊8月3 MUSIC角度估计算法从上节可知,由于发射阵元产生不同频率的信号,因此在接收阵元回波上存在一定的频差-距离相位差,在应用算法前首先对其进行预处理补偿,然后使用MUSC算法进行测角。图2阵列形状示意图定义等效阵列的协方差矩阵为4.1动目标等速不等距R=EX]=A(的RA"(O)+an1实验中将2个日标速度设置为等速10ms,其距离和角度参数分别为(30m,2.59)、40m59)。其频其中R为信号协方差矩阵,σ衣示噪声功率,/谱和角度估计结果如图3、图4所示。为单位矩阵。对其进行特征值分解可得R=EAE +EME其中λ,e;(i=1,2,…,MN)分别为升序排列的矩阵R的特征值和对应的特征向量En=span{e,e2,…和E、=span{eM-p+,eM-p+2,……,M}分别表小矩阵R的信号子空间和噪声子空间。在该情况下的 MUSIC空间谱定义为MUSIC(6)=[A( DEEnA(O)(8)由于存在噪声,上式倒数中的数值不为零,而是个很小的值,所以 MUSIC空间谱会出现一个尖峰。通过对θ的不断交化进行谱峰搜索来估计波达角。图3上/下扫频频谱图4计算机仿真本文以毫米波段的多载频MIMO雷达为例,传统雷达取上/下扫频周期为T=10ms,调频带宽B=500MHz,阵列形状如图2所示,发射天线为均匀线阵,间距d=元/2,阵元数M=2,天线问的频率间隔△f=100MHz,发射天线1、2的载波频率分别为77GHz77.1GH。模型采取线阵的结构,且发射天线分别布置在接收天线两端,接收天线间距d=4/2,阵元数N=8图4速度相同距离不同时角度估计结果接收阵元与发射阵元间距h=4/2,所以由MIMO雷达特性可知,最后得到的虚拟阵元数为通过仿真,测得动目标距离和速度信息为MN=16。(30.019m,10.034ms),(40.293m,9.981ms)=MMO为了验证本文所提方法的有效性,本文同时雷达角度佔计仿真结果为(-497°,-2.519),传统雷达引入相同参数指标的单发多收传统雷达做仿真对角度估计结果为(-336°)比实验。216?1994-2018ChinaacAdcmicJOurnalElcctronicPublishingHousc.Allrightsrescrvcd.http://www.cnki.nct微波学报2017年第33卷增刊8月4,2动目标等距不等速进一步验证了该方法的可行性,非常适合运用在实验,将2个日标距离设置为等距35m,汽车防撞雷达这种高分辨、小型化系统上其速度和角度参数分别为(10ms,2.5°),(15m/s,59)。参考文献其最终角度仿真图如图5所示。l]韩峻峰,张惠敏,潘盛辉,林川汽车防撞雷达概述「J广西科技大学学报.201,22(4):54-58传线雨达[2]侯宪美.多载频MIMO高频雷达的波束形成方法硏究[D,哈尔滨工业大学,2014[3]杨明磊,张守宏,陈伯孝,张焕颖.多载频MMO雷达的一和新的信号处理方法山电子信息学报,2009,31(1):147-151[4]田燕妮,张杨,徐晶晶.MMO技术舰载反导探测系统构成方法[J兵工自动化2015(1)4-6图5距离相同速度不同时角度估计结果[5]党宏社,韩崇昭,赵广社.汽车毫米波FMCW雷达中频由仿真结果可知,测得动目标距离和速度信信号的采样与处理[J现代雷达,200,24(4)43-45息为(29.879m,10.07mns),(30.155m,15.102ms)「6]魏星,万建伟,皇甫堪基于长短基线干涉仪的无源定MMO雷达角度估计仿真结果为(-5.01°,2.499),传位系统研究叮现代雷达,2007,29(5):22-25.统雷达角度估计结果为(4.87°,-2739)[7 KRIM H, VIBERG M. Two decades of array signal通过上述两个实验可以看出,在目标邻近的processing research the parametric approach[]. Signal情况下,MIMO雷达可以清晰地分辨出目标所在Proccssing Magazine, IEEE, 1996, 13(4): 67-94方向,而传统雷达则无法区分两个日标或分辨较[8 Duofang C, Baixiao C, Guodong Q. Angle estimation差using ESPrit in MIMO Radar[J. Electron Lett,2008:44(12):770-7715结论9]杨晓玉冯大政. ESPRIT算法在单基地MMO雷达中的应用[J电子科技,2009,22(12)91-本文讨论了7Hz多载频MIMO汽车雷达的[10染浩,崔,代林,余剑.阵列误差条件下MMo信号处理方法,运用线性调频连续波的近程探测雷达测向敏感性分析[微波学报,2015,31(41-8优点特性精准地测量出目标的距离和速度信息并通过MUSC算法准确测量出多运动目标相对季晓宇男,193年生,硕士研究生。主要研究方向:微应的角度,达到精确测向的理想效果,仿真结果波毫米波汽车雷达信号处珥,MMO雷达信号处理。证明了该方汯精度可以达到001°。在系统体积和Em-9191562@q40m参数不变的情况下,利用与传统雷达的仿真对比217?1994-2018ChinaacAdcmicJOurnalElcctronicPublishingHousc.Allrightsrescrvcd.http://www.cnki.nct
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系统辨识大牛Ljung编写的MATLAB系统辨识使用手册
系统辨识大牛Ljung编写的MATLAB系统辨识使用手册，这本书详细地介绍了在MATLAB已经所属simulink环境下，系统辨识工具箱的一些使用办法，是一本非常经典的教材！Revision Historypril 1988First printingJuly 1991Second printingMay1995Third printingNovember 2000 Fourth printingRevised for Version 5.0(Release 12)pril 2001Fifth printingJuly 2002Online onlyRevised for Version 5.0.2 Release 13)June 2004Sixth printingRevised for Version 6.0.1(Release 14)March 2005Online onlyRevised for Version 6.1.1Release 14SP2)September 2005 Seventh printingRevised for Version 6.1.2(Release 14SP3)March 2006Online onlyRevised for Version 6.1.3(Release 2006a)September 2006 Online onlyRevised for Version 6.2 Release 2006b)March 2007Online onlyRevised for Version 7.0 ( Release 2007a)September 2007 Online onlyRevised for Version 7.1 (Release 2007bMarch 2008Online onlyRevised for Version 7.2(Release 2008a)October 2008Online onlyRevised for Version 7.2.1 Release 2008b)March 2009Online onlyRevised for Version 7.3(Release 2009a)September 2009 Online onlyRevised for Version 7.3.1(Release 2009b)March 2010Online onlyRevised for Version 7. 4 (Release 2010a)eptember2010 Online onlyRevised for Version 7.4.1(Release 2010b)pril 2011Online onlRevised for Version 7.4.2(Release 2011a)September 2011 Online onlyRevised for Version 7.4.3(Release 2011b)March 2012Online onlyRevised for Version 8.0( Release 2012aabout the DevelopersAbout the Developersystem Identification Toolbox software is developed in association with thefollowing leading researchers in the system identification fieldLennart Ljung. Professor Lennart Ljung is with the department ofElectrical Engineering at Linkoping University in Sweden. He is a recognizedleader in system identification and has published numerous papers and booksin this areaQinghua Zhang. Dr. Qinghua Zhang is a researcher at Institut Nationalde recherche en Informatique et en Automatique(INria) and at Institut deRecherche en Informatique et systemes Aleatoires (Irisa), both in rennesFrance. He conducts research in the areas of nonlinear system identificationfault diagnosis, and signal processing with applications in the fields of energyautomotive, and biomedical systemsPeter Lindskog. Dr. Peter Lindskog is employed by nira dynamiAB, Sweden. He conducts research in the areas of system identificationsignal processing, and automatic control with a focus on vehicle industryapplicationsAnatoli Juditsky. Professor Anatoli Juditsky is with the laboratoire JeanKuntzmann at the Universite Joseph Fourier, Grenoble, france. He conductsresearch in the areas of nonparametric statistics, system identification, andstochastic optimizationAbout the developersContentsChoosing Your System Identification ApproachLinear model structures1-2What Are Model objects?Model objects represent linear systemsAbout model data1-5Types of Model objectsDynamic System Models1-9Numeric Models1-11umeric Linear Time Invariant (LTD Models1-11Identified LTI modelsIdentified Nonlinear models1-12Nonlinear model structures1-13Recommended Model Estimation Sequence1-14Supported Models for Time- and Frequency-DomainData,,,,,,,1-16Supported Models for Time-Domain Data1-16Supported Models for Frequency-Domain Data1-17See also1-18Supported Continuous-and Discrete-Time Models1-19Model estimation commands1-21Creating Model Structures at the command Line ... 1-22about system Identification Toolbox Model Objects ... 1-22When to Construct a Model Structure Independently ofEstimation1-23Commands for Constructing Model Structures1-24Model Properties1-25See als1-27Modeling Multiple-Output Systems ......... 1-28About Modeling multiple-Output Systems1-28Modeling Multiple Outputs Directly1-29Modeling multiple outputs as a Combination ofSingle-Output Models.......1-29Improving Multiple-Output Estimation Results byWeighing Outputs During Estimation ....... 1-30Identified linear Time-Invariant models1-32IDLTI Models1-32Configuration of the Structure of Measured and Noise oRepresentation of the Measured and noise Components foVarious model Types1-33Components ....1-35Imposing Constraints on the Values of ModeParameters1-37Estimation of Linear models1-8Data Import and Processing2「Supported Data ...2-3Ways to Obtain Identification DataWays to Prepare Data for System Identification ... 2-6Requirements on Data SamplingRepresenting Data in MATLAB Workspace·····Time-Domain Data Representation2-9Time-Series Data Representation2-10ContentsFrequency-Domain Data Representation ....... 2-11Importing Data into the Gui2-17Types of Data You Can import into the GUi2-17Importing time-Domain Data into the GUI2-18Importing Frequency-Domain Data into the GUI2-22Importing Data Objects into the GUI ......... 2-30Specifying the data sampling interval2-34Specifying estimation and validation Data2-35Preping data Using Quick StartCreating Data Sets from a Subset of Signal Channelo2-362-37Creating multiexperiment Data Sets in the gUi2-39Managing data in the gui ............. 2-46Representing Time- and Frequency-Domain Data Usingiddata object2-55iddata constructor2-55iddata Properties.........2-58Creating Multiexperiment Data at the Command Line .. 2-61Select Data Channels, I/O Data and Experiments in iddataObjects2-63Increasing Number of Channels or Data Points of iddataObjects2-67Managing iddata Objects2-69Representing Frequency-Response Data Using idfrdObiec2-76idfrd Constructor2-76idfrd Properties2-77Select I/o Channels and Data in idfrd Objects ..... 2-79Adding Input or Output Channels in idfrd Objects2-80Managing idfrd Objects2-83Operations That Create idfrd Objects2-83Analyzing Data quality2-85Is your data ready for modeling?2-85Plotting Data in the guI Versus at the command line2-86How to plot data in the gui2-86How to plot data at the command line2-92How to Analyze Data Using the advice Command2-94Selecting Subsets of Data2-96IXWhy Select Subsets of Data?2-96Extract Subsets of Data Using the GUI2-97Extract Subsets of data at the Command Line2-99Handling Missing Data and outliers2-100Handling missing data2-100Handling outliers2-101Extract and Model Specific Data Segments2-102See also2-103Handling offsets and Trends in Data2-104When to detrend data2-104Alternatives for Detrending Data in GUi or at theCommand-Line2-105Next Steps After detrending2-107How to Detrend Data Using the Gui2-108How to detrend data at the Command line2-109Detrending Steady-State Dat109cending transient Dat2-109See also2-110Resampling Data2-111What Is resampling?...,,.,,,,,,,,,,,.2-111Resampling data without Aliasing Effects2-112See also2-116Resampling data Using the GUi.,,,,2-117Resampling Data at the Command line2-118Filtering Data2-120Supported Filters2-120Choosing to Prefilter Your Data2-120See also2-121How to Filter Data Using the gui2-122Filtering Time-Domain Data in the GuI........ 2-122Content
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