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自动驾驶的眼睛:一文看懂TDM FMCW MIMO雷达与“虚拟阵列”黑科技【附全部MATLAB代码】

  • 2026-10-22 04:47:08
自动驾驶的眼睛:一文看懂TDM FMCW MIMO雷达与“虚拟阵列”黑科技【附全部MATLAB代码】

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🚗 自动驾驶的眼睛:一文看懂TDM FMCW MIMO雷达与“虚拟阵列”黑科技

在自动驾驶和高级辅助驾驶系统(ADAS)中,77GHz毫米波雷达是不可或缺的“眼睛”。但你是否想过,在高速行驶中,当两辆并排行驶的汽车距离很近时,雷达是如何将它们“看”作两个独立目标,而不是糊成一团的?

.rtcContent { padding: 30px; } .lineNode {font-size: 16pt; font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-style: normal; font-weight: normal; }%TDM MIMO Radar signal processing chain basicsfc = 77e9; %typical center freq for automotive radarsc = 3e8;lambda = c/fc;Nt = 2; %no of trasnmittersNr = 4; %no of recievers%defining array characteristicsdt = Nr*lambda/2; %thin array spacingdr = lambda/2; %full array spacingtxarray = phased.ULA(Nt,dt);rxarray = phased.ULA(Nr,dr);ang = -90:90; %field of view (FOV)pattx = pattern(txarray,fc,ang,0,'Type','powerdb');patrx = pattern(rxarray,fc,ang,0,'Type','powerdb');pat2way = pattx+patrx;%Demonstrating the equivalence of the two-way pattern this system %with a virual array of Nt * Nr elements varray = phased.ULA(Nt*Nr,dr);patv = pattern(varray,fc,ang,0,'Type','powerdb');figure(1)helperPlotMultipledBPattern(ang,[pat2way patv],[-30 0],...    {'Two-way Pattern','Virtual Array Pattern'},...    'Patterns of thin/full arrays and virtual array',...    {'-','--'},[1 2]);%TDM-MIMO Radar Simulation%Defining Waveformwaveform = helperDesignFMCWWaveform(c,lambda);fs = waveform.SampleRate;tm = waveform.SweepTime;sig = waveform();figure(2)subplot(211); plot(0:1/fs:tm-1/fs,real(sig));xlabel('Time (s)'); ylabel('Amplitude (v)');title('FMCW signal'); axis tight;subplot(212); spectrogram(sig,32,16,32,fs,'yaxis');title('FMCW signal spectrogram');%Radar System Setup%only main components are modeled and the effects from other components%are omitted, such as coupler and mixer. % In addition, the antenna is assumed to be isotropic.transmitter = phased.Transmitter('PeakPower',0.001,'Gain',36);receiver = phased.ReceiverPreamp('Gain',40,'NoiseFigure',4.5,'SampleRate',fs);txradiator = phased.Radiator('Sensor',txarray,'OperatingFrequency',fc,...    'PropagationSpeed',c,'WeightsInputPort',true);rxcollector = phased.Collector('Sensor',rxarray,'OperatingFrequency',fc,...    'PropagationSpeed',c);%The Radar has two cars in the FOV with a separation of 20 degrees%Define position and motion of the ego vehicle and the two cars in the FOVradar_speed = 100*1000/3600;     % Ego vehicle speed 100 km/hradarmotion = phased.Platform('InitialPosition',[0;0;0.5],'Velocity',[radar_speed;0;0]);car_dist = [40 50];              % Distance between sensor and cars (meters)car_speed = [-80 96]*1000/3600;  % km/h -> m/scar_az = [-10 10];car_rcs = [20 40];car_pos = [car_dist.*cosd(car_az);car_dist.*sind(car_az);0.5 0.5];cars = phased.RadarTarget('MeanRCS',car_rcs,'PropagationSpeed',c,'OperatingFrequency',fc);carmotion = phased.Platform('InitialPosition',car_pos,'Velocity',[car_speed;0 0;0 0]);%Channel model is assumed to be free space.channel = phased.FreeSpace('PropagationSpeed',c,...    'OperatingFrequency',fc,'SampleRate',fs,'TwoWayPropagation',true);%Generating the radar data cube recieved by the radarrng(2017);Nsweep = 64;Dn = 2;      % Decimation factorfs = fs/Dn;xr = complex(zeros(fs*waveform.SweepTime,Nr,Nsweep));w0 = [0;1];  % weights to enable/disable radiating elementsfor m = 1:Nsweep    % Update radar and target positions    [radar_pos,radar_vel] = radarmotion(waveform.SweepTime);    [tgt_pos,tgt_vel] = carmotion(waveform.SweepTime);    [~,tgt_ang] = rangeangle(tgt_pos,radar_pos);    % Transmit FMCW waveform    txsig = transmitter(sig);    % Toggle transmit element    w0 = 1-w0;     txsig = txradiator(txsig,tgt_ang,w0);    % Propagate the signal and reflect off the target    txsig = channel(txsig,radar_pos,tgt_pos,radar_vel,tgt_vel);    txsig = cars(txsig);    % Dechirp the received radar return    rxsig = rxcollector(txsig,tgt_ang);    rxsig = receiver(rxsig);        dechirpsig = dechirp(rxsig,sig);    % Decimate the return to reduce computation requirements    for n = size(xr,2):-1:1        xr(:,n,m) = decimate(dechirpsig(:,n),Dn,'FIR');    endend%converting the radar data cube generated from the physical array%to the one formed using the virtual arrayxr1 = xr(:,:,1:2:end); %cube recieved by first transmit antennaxr2 = xr(:,:,2:2:end); %cube recieved by first transmit antennaxrv = cat(2,xr1,xr2); %virtual radar data cube%performing range-Doppler processing on the virtual data cube.nfft_r = 2^nextpow2(size(xrv,1));nfft_d = 2^nextpow2(size(xrv,3));rngdop = phased.RangeDopplerResponse('PropagationSpeed',c,...    'DopplerOutput','Speed','OperatingFrequency',fc,'SampleRate',fs,...    'RangeMethod','FFT','PRFSource','Property',...    'RangeWindow','Hann','PRF',1/(Nt*waveform.SweepTime),...    'SweepSlope',waveform.SweepBandwidth/waveform.SweepTime,...    'RangeFFTLengthSource','Property','RangeFFTLength',nfft_r,...    'DopplerFFTLengthSource','Property','DopplerFFTLength',nfft_d,...    'DopplerWindow','Hann');[resp,r,sp] = rngdop(xrv);%the range-Doppler map for the first element in the virtual arrayfigure(3)plotResponse(rngdop,squeeze(xrv(:,1,:)));%performing detection using a manual threshold deduced from%the range-doppler maprespmap = squeeze(mag2db(abs(resp(:,1,:))));ridx = helperRDDetection(respmap,-10);%extracting range cuts of targets for further spatial processingxv = squeeze(sum(resp(ridx,:,:),1))';doa = phased.BeamscanEstimator('SensorArray',varray,'PropagationSpeed',c,...    'OperatingFrequency',fc,'DOAOutputPort',true,'NumSignals',2,'ScanAngles',ang);[Pdoav,target_az_est] = doa(xv);fprintf('target_az_est = [%s]\n',num2str(target_az_est));%demonstrating angular resolution improvment achieved by virtual arraydoarx = phased.BeamscanEstimator('SensorArray',rxarray,'PropagationSpeed',c,...    'OperatingFrequency',fc,'DOAOutputPort',true,'ScanAngles',ang);Pdoarx = doarx(xr);figure(4)helperPlotMultipledBPattern(ang,mag2db(abs([Pdoav Pdoarx])),[-30 0],...    {'Virtual Array','Physical Array'},...    'Spatial spectrum for virtual array and physical array',{'-','--'});

今天,我们就通过一个完整的MATLAB实战案例,硬核解析TDM FMCW MIMO雷达的信号处理链,并揭开它提升角分辨率的终极武器——虚拟阵列。

📡 1. 雷达系统基操:FMCW波形

汽车雷达通常采用调频连续波(FMCW)。简单来说,就是发射一个频率随时间线性增加的“啾啾”声。通过测量回波与发射信号的频率差(拍频),就能同时算出目标的距离和速度。

在本案例中,雷达参数设定为典型的77GHz频段:

  • 最大测距:200米

  • 距离分辨率:1米(对应150MHz扫频带宽)

  • 最大测速:230 km/h

🧠 2. 核心黑科技:MIMO与“虚拟阵列”

普通雷达想要看得清楚,只能靠增加天线数量,但这会导致硬件成本和体积飙升。MIMO(多输入多输出)雷达提供了一种“作弊”级别的解法。

假设我们的雷达有 2个发射天线 和 4个接收天线。

  • TDM(时分复用):为了保证发射信号互不干扰,2个发射天线采取“轮流发言”的策略(TDM)。

  • 虚拟阵列的诞生:当TX1发射时,4个接收天线RX1-RX4收到信号;当TX2发射时,4个接收天线再次收到信号。由于TX1和TX2在空间上存在间隔,这两组接收信号在物理上等效于一个包含 8个天线阵元 的线性阵列!

划重点:物理上我们只用了6个天线(2发4收),但在信号处理层面,我们凭空“变”出了8个通道的数据。这就是虚拟阵列。

从下图的仿真结果可以看出,物理阵列的双程方向图与虚拟阵列的方向图完美重合,但虚拟阵列的波束更窄,角分辨率直接翻倍!

💻 3. MATLAB信号处理全链路拆解

光说不练假把式,我们设定一个极端场景:前方视场(FOV)中,有两辆车分别位于方位角 -10° 和 10°(间距20°),自车速度100km/h。看看MIMO雷达如何把它们揪出来。

Step 1:生成雷达数据立方体

雷达以64个扫频周期为一帧,循环采集数据。由于采用了TDM,奇数周期由TX1发射,偶数周期由TX2发射。我们将采集到的原始数据重排,把TX1和TX2对应的数据拼接到一起,直接构建出虚拟阵列的雷达数据立方体。

Step 2:距离-多普勒处理 (2D-FFT)

对虚拟阵列的快时间(距离)和慢时间(多普勒)分别做FFT,得到经典的距离-多普勒图。在这个图上,我们可以清晰地看到两个目标点,并得出它们的精确距离和相对速度。

Step 3:DOA估计(波达方向)- 见证奇迹的时刻

提取目标对应的距离单元数据,送入波束扫描算法来估计角度。

  • 普通物理阵列(4通道):由于物理孔径受限,在空间谱上,-10°和10°的两个目标糊在了一起,无法分辨!

  • MIMO虚拟阵列(8通道):在空间谱上出现了两个极其尖锐且独立的峰值!算法准确输出目标方位角:[-10.0000, 10.0000]。完美分辨!

🌟 总结

在这个TDM FMCW MIMO雷达的实战中,我们走通了从波形设计 -> 回波模拟 -> 距离多普勒处理 -> 虚拟阵列构建 -> DOA估计的全流程。

虚拟阵列技术的意义在于:它在不增加接收通道硬件复杂度的前提下,利用多发射天线的空间分集,成倍地增加了等效孔径,打破了传统雷达角分辨率的物理瓶颈。这正是现代高性能4D毫米波雷达能够实现高精度点云输出的核心密码。

.rtcContent { padding: 30px; } .lineNode {font-size: 16pt; font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-style: normal; font-weight: normal; }function [rangeidx,dopidx] = helperRDDetection(respmap,th)% This function helperRDDetection is only in support of% MIMORadarVirtualArrayExample. It may change in a future release.% Copyright 2017 The MathWorks, Inc.respmap = respmap-max(respmap(:));                     % Normalize mappeakmat = phased.internal.findpeaks2D(respmap,0,th);   [rangeidx,dopidx] = ind2sub(size(respmap),find(peakmat));
.rtcContent { padding: 30px; } .lineNode {font-size: 16pt; font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-style: normal; font-weight: normal; }function [rangeidx,dopidx] = helperRDDetection(respmap,th)% This function helperRDDetection is only in support of% MIMORadarVirtualArrayExample. It may change in a future release.% Copyright 2017 The MathWorks, Inc.respmap = respmap-max(respmap(:));                     % Normalize mappeakmat = phased.internal.findpeaks2D(respmap,0,th);   [rangeidx,dopidx] = ind2sub(size(respmap),find(peakmat));
.rtcContent { padding: 30px; } .lineNode {font-size: 16pt; font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-style: normal; font-weight: normal; }function wav = helperDesignFMCWWaveform(c,lambda)% This function helperDesignFMCWWaveform is only in support of% MIMORadarVirtualArrayExample. It may change in a future release.% Copyright 2017 The MathWorks, Inc.range_max = 200;tm = 5.5*range2time(range_max,c);range_res = 1;bw = rangeres2bw(range_res,c);sweep_slope = bw/tm;fr_max = range2beat(range_max,sweep_slope,c);v_max = 230*1000/3600;fd_max = speed2dop(2*v_max,lambda);fb_max = fr_max+fd_max;fs = max(2*fb_max,bw);wav = phased.FMCWWaveform('SweepTime',tm,'SweepBandwidth',bw,...    'SampleRate',fs);

👇 互动时间

如果你对毫米波雷达信号处理、MIMO阵列设计或者MATLAB仿真感兴趣,想要获取本文配套的完整源码(包含波形设计、虚拟阵列转换、2D-FFT等核心脚本),欢迎在评论区留言“雷达”,或者后台私信交流!

免责声明:本文代码及原理基于MATLAB Phased Array System Toolbox开源示例解析,仅供学习交流。

#自动驾驶 #毫米波雷达 #MIMO #信号处理 #MATLAB #虚拟阵列 #汽车电子

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