合成孔径雷达回波数据处理MATLAB程序

Posted 2023-04-18

各位朋友，谁能帮忙提供以下合成孔径雷达回波信号处理的MATLAB程序。本人非常需要，最好是CS算法（Chirp-Scaling）。没有的话RD算法也可以。我会追加一倍的分数，非常感谢。

参考技术A CS
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%产生Stripmap SAR的回波
clear all
clc
thetaT=0;%T平台波束斜视角
thetaT=thetaT*pi/180;%rad
thetaR=0;%R平台波束斜视角
thetaR=thetaR*pi/180;
c=3e8;%光速
fc=1.5e9;%载频
lambda=c/fc;%波长

%%测绘带区域
X0=200;%方位向[-X0,X0]
Rtc=3000;
Rrc=3000;
Rc=(Rtc+Rrc)/2;
R0=150;%距离向[Rc-R0,Rc+R0]

%%距离向(Range),r/t domain
Tr=1.33e-6;%LFM信号脉宽1.33us (200m)
Br=150e6; %LFM信号带宽 150MHz
Kr=Br/Tr; %调频斜率
Nr=1024;
r=Rc+linspace(-R0,R0,Nr);
t=2*r/c;%t域序列
dt=R0*4/c/Nr;%采样周期
f=linspace(-1/2/dt,1/2/dt,Nr);%f域序列

%%方位向(Azimuth,Cross-Range),x/u domain
v=100;%SAR 平台速度
Lsar=300;%合成孔径长度
Na=512;
x=linspace(-X0,X0,Na);%u域序列
u=x/v;
du=2*X0/v/Na;
fu=linspace(-1/2/du,1/2/du,Na);%fu域序列
ftdc=v*sin(thetaT);
ftdr=-(v*cos(thetaT))^2/lambda/Rtc;
frdc=v*sin(thetaR);
frdr=-(v*cos(thetaR))^2/lambda/Rrc;
fdc=ftdc+frdc;%Doppler调频中心频率
fdr=ftdr+frdr;%Doppler调频斜率
%％目标位置
Ntar=3;%目标个数
Ptar=[Rrc,0,1 %距离向坐标,方位向坐标,sigma
Rrc+50,-50,1
Rrc+50,50,1];

%%产生回波
s_ut=zeros(Nr,Na);
U=ones(Nr,1)*u;%扩充为矩阵
T=t'*ones(1,Na);
for i=1:1:Ntar
rn=Ptar(i,1);xn=Ptar(i,2);sigma=Ptar(i,3);
rtn=rn+Rtc-Rrc;
RT=sqrt(rtn^2+(rtn*tan(thetaT)+xn-v*U).^2);
RR=sqrt(rn^2+(rn*tan(thetaT)+xn-v*U).^2);
R=RT+RR;
DT=T-R/c;
phase=-pi*Kr*DT.^2-2*pi/lambda*R;
s_ut=s_ut+sigma*exp(j*phase).*(abs(DT)<Tr/2).*(abs(v*U-xn)<Lsar/2);
end;

%方位向fft
s_kt=fftshift(fft(fftshift(s_ut).')).';

%CS变换
kc=4*pi/lambda;
kc=kc*ones(1,Na);
kx=fu/v;
p_kx0=-sqrt(kc.^2-kx.^2);%相位项泰勒展开的系数函数
p_kx1=2*kc/c/p_kx0;
p_kx2=-2.*kx.^2/c^2./p_kx0.^3;
C_kx=-(c*p_kx1/2+1);
Ks_r=1-2*Kr*Rc.*p_kx2;
Ks_kx_r=Kr/pi./Ks_r;
r0=Rc;
s2_ut=exp(j*pi*C_kx.*ones(Nr,1)*Ks_kx_r.*(t'*ones(1,Na)-2*r0*(1+C_kx)/c).^2);%设计的线性调频信号

S_cs=s_kt.*s2_ut;

%距离向fft
S_kw=fftshift(fft(fftshift(S_cs)));

%距离向匹配滤波
w=2*pi*f;
rmc_r=exp(j.*w*2*C_kx*r0/c).*exp(j.*w.^2/4/pi/Kr/(1+C_kx));
rmc_r=rmc_r'*ones(1,Na);
S_rmc=S_kw.*rmc_r;

%距离向ifft
S_kt=fftshift(ifft(fftshift(S_rmc)));
d_kxr=4*pi/c^2*Kr*C_kx*(1+C_kx).*(Rc-r0).^2;%CS变换带来的相位误差

S_kt=S_kt.*exp(-j*d_kxr);%消除相位误差

%方位向匹配滤波
FU=ones(Nr,1)*fu;
H_kx=exp(j*pi/fdr*(FU-fdc).^2);%方位向压缩因子
I_ut=S_kt.*H_kx;
I_ut=fftshift(ifft(fftshift(I_ut.'))).';

subplot(221)
G=20*log10(abs(s_ut)+1e-6);
gm=max(max(G));
gn=gm-40;%显示动态范围40dB
G=255/(gm-gn)*(G-gn).*(G>gn);
imagesc(x,r-Rc,-G),colormap(gray)
grid on,axis tight,
xlabel('Azimuth')
ylabel('Range')
title('(a)原始信号')

subplot(222)
G=20*log10(abs(S_rmc)+1e-6);
gm=max(max(G));
gn=gm-40;%显示动态范围40dB
G=255/(gm-gn)*(G-gn).*(G>gn);
imagesc(x,r-Rc,-G),colormap(gray)
grid on,axis tight,
xlabel('Azimuth')
ylabel('Range')
title('(b)距离向匹配滤波后频谱')

subplot(223)
G=20*log10(abs(S_kt)+1e-6);
gm=max(max(G));
gn=gm-40;%显示动态范围40dB
G=255/(gm-gn)*(G-gn).*(G>gn);
imagesc(x,r-Rc,G),colormap(gray)
grid on,axis tight,
xlabel('Azimuth')
ylabel('Range')
title('(c)消除相位误差后频谱')

subplot(224)
G=20*log10(abs(I_ut)+1e-6);
gm=max(max(G));
gn=gm-60;%显示动态范围40dB
G=255/(gm-gn)*(G-gn).*(G>gn);
imagesc(x,r-Rc,G),colormap(gray)
grid on,axis tight,
xlabel('Azimuth')
ylabel('Range')
title('(d)目标图象')
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
RD
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%产生Stripmap SAR的回波
clear all

thetaT=0;%T平台波束斜视角
thetaT=thetaT*pi/180;%rad
thetaR=0;%R平台波束斜视角
thetaR=thetaR*pi/180;
c=3e8;%光速
fc=1.5e9;%载频
lambda=c/fc;%波长

%%测绘带区域
X0=200;%方位向[-X0,X0]
Rtc=3000;
Rrc=3000;
Rc=(Rtc+Rrc)/2;
R0=150;%距离向[Rc-R0,Rc+R0]

%%距离向(Range),r/t domain
Tr=1.5e-6;%LFM信号脉宽 1.5us (200m)
Br=150e6; %LFM信号带宽 150MHz
Kr=Br/Tr; %调频斜率
Nr=512;
r=Rc+linspace(-R0,R0,Nr);
t=2*r/c;%t域序列
dt=R0*4/c/Nr;%采样周期
f=linspace(-1/2/dt,1/2/dt,Nr);%f域序列

%%方位向(Azimuth,Cross-Range),x/u domain
v=100;%SAR 平台速度
Lsar=300;%合成孔径长度
Na=1024;
x=linspace(-X0,X0,Na);%u域序列
u=x/v;
du=2*X0/v/Na;
fu=linspace(-1/2/du,1/2/du,Na);%fu域序列
ftdc=v*sin(thetaT);
ftdr=-(v*cos(thetaT))^2/lambda/Rtc;
frdc=v*sin(thetaR);
frdr=-(v*cos(thetaR))^2/lambda/Rrc;
fdc=ftdc+frdc;%Doppler调频中心频率
fdr=ftdr+frdr;%Doppler调频斜率
%％目标位置
Ntar=3;%目标个数
Ptar=[Rrc,0,1 %距离向坐标,方位向坐标,sigma
Rrc+50,-50,1
Rrc+50,50,1];

%%产生回波
s_ut=zeros(Nr,Na);
U=ones(Nr,1)*u;%扩充为矩阵
T=t'*ones(1,Na);
for i=1:1:Ntar
rn=Ptar(i,1);xn=Ptar(i,2);sigma=Ptar(i,3);
rtn=rn+Rtc-Rrc;
RT=sqrt(rtn^2+(rtn*tan(thetaT)+xn-v*U).^2);
RR=sqrt(rn^2+(rn*tan(thetaT)+xn-v*U).^2);
R=RT+RR;
DT=T-R/c;
phase=pi*Kr*DT.^2-2*pi/lambda*R;
s_ut=s_ut+sigma*exp(j*phase).*(abs(DT)<Tr/2).*(abs(v*U-xn)<Lsar/2);
end;

%%距离压缩
p0_t=exp(j*pi*Kr*(t-2*Rc/c).^2).*(abs(t-2*Rc/c)<Tr/2);%距离向LFM信号
p0_f=fftshift(fft(fftshift(p0_t)));
s_uf=fftshift(fft(fftshift(s_ut)));%距离向FFT
src_uf=s_uf.*(conj(p0_f).'*ones(1,Na));%距离压缩
src_ut=fftshift(ifft(fftshift(src_uf)));%距离压缩后的信号
src_fut=fftshift(fft(fftshift(src_ut).')).';%距离多普勒域

%%二次距离压缩,距离迁移校正原理仿真
src_fuf=fftshift(fft(fftshift(src_uf).')).';%距离压缩后的二维频谱
F=f'*ones(1,Na);%扩充为矩阵
FU=ones(Nr,1)*fu;
p0_2f=exp(j*pi/fc^2/fdr*(FU.*F).^2+j*pi*fdc^2/fc/fdr*F-j*pi/fc/fdr*FU.^2.*F);
s2rc_fuf=src_fuf.*p0_2f;
s2rc_fut=fftshift(ifft(fftshift(s2rc_fuf)));%距离多普勒域

%%方位压缩
p0_2fu=exp(j*pi/fdr*(FU-fdc).^2);%方位向压缩因子
s2rcac_fut=s2rc_fut.*p0_2fu;%方位压缩
s2rcac_fuf=fftshift(fft(fftshift(s2rcac_fut)));%距离方位压缩后的二维频谱
s2rcac_ut=fftshift(ifft(fftshift(s2rcac_fut).')).';%方位向IFFT

subplot(221)
G=20*log10(abs(s_ut)+1e-6);
gm=max(max(G));
gn=gm-40;%显示动态范围40dB
G=255/(gm-gn)*(G-gn).*(G>gn);
imagesc(x,r-Rc,-G),colormap(gray)
grid on,axis tight,
xlabel('Azimuth')
ylabel('Range')
title('(a)原始信号')

subplot(222)
G=20*log10(abs(src_fut)+1e-6);
gm=max(max(G));
gn=gm-40;%显示动态范围40dB
G=255/(gm-gn)*(G-gn).*(G>gn);
imagesc(fu,r-Rc,-G),colormap(gray)
grid on,axis tight,
xlabel('Azimuth')
ylabel('Range')
title('(b)距离多普勒域频谱')

subplot(223)
G=20*log10(abs(s2rc_fut)+1e-6);
gm=max(max(G));
gn=gm-40;%显示动态范围40dB
G=255/(gm-gn)*(G-gn).*(G>gn);
imagesc(fu,r-Rc,-G),colormap(gray)
grid on,axis tight,
xlabel('Azimuth')
ylabel('Range')
title('(c)RMC后的RD域频谱')

subplot(224)
G=20*log10(abs(s2rcac_ut)+1e-6);
gm=max(max(G));
gn=gm-60;%显示动态范围40dB
G=255/(gm-gn)*(G-gn).*(G>gn);
imagesc(x,r-Rc,G),colormap(gray)
grid on,axis tight,
xlabel('Azimuth')
ylabel('Range')
title('(d)目标图象')追问

怎么直接复制了一个点目标回波程序阿。还是辛苦你了。

追答

我做点目标，你参考下！

追问

我想要数据处理的那程序，你会吗？点目标的我已经有了。

本回答被提问者采纳

智能驾驶基于合成雷达的传感器融合的智能驾驶MATLAB仿真

% Define an empty scenario.
scenario = drivingScenario;
scenario.SampleTime = 0.01;

%% 
% Add a stretch of 500 meters of typical highway road with two lanes. The 
% road is defined using a set of points, where each point defines the center of 
% the road in 3-D space. 
roadCenters = [0 0; 50 0; 100 0; 250 20; 500 40];
road(scenario, roadCenters, 'lanes',lanespec(2));

%% 
% Create the ego vehicle and three cars around it: one that overtakes the
% ego vehicle and passes it on the left, one that drives right in front of
% the ego vehicle and one that drives right behind the ego vehicle. All the
% cars follow the trajectory defined by the road waypoints by using the
% |trajectory| driving policy. The passing car will start on the right
% lane, move to the left lane to pass, and return to the right lane.

% Create the ego vehicle that travels at 25 m/s along the road.  Place the
% vehicle on the right lane by subtracting off half a lane width (1.8 m)
% from the centerline of the road.
egoCar = vehicle(scenario, 'ClassID', 1);
trajectory(egoCar, roadCenters(2:end,:) - [0 1.8], 25); % On right lane

% Add a car in front of the ego vehicle
leadCar = vehicle(scenario, 'ClassID', 1);
trajectory(leadCar, [70 0; roadCenters(3:end,:)] - [0 1.8], 25); % On right lane

% Add a car that travels at 35 m/s along the road and passes the ego vehicle
passingCar = vehicle(scenario, 'ClassID', 1);
waypoints = [0 -1.8; 50 1.8; 100 1.8; 250 21.8; 400 32.2; 500 38.2];
trajectory(passingCar, waypoints, 35);

% Add a car behind the ego vehicle
chaseCar = vehicle(scenario, 'ClassID', 1);
trajectory(chaseCar, [25 0; roadCenters(2:end,:)] - [0 1.8], 25); % On right lane

%% Define Radar Sensors
% Simulate an ego vehicle that has 6 radar sensors and
% The ego vehicle is equipped with a
% long-range radar sensor. Each side of the vehicle has two short-range radar
% sensors, each covering 90 degrees. One sensor on each side covers from
% the middle of the vehicle to the back. The other sensor on each side
% covers from the middle of the vehicle forward. The figure in the next
% section shows the coverage.

sensors = cell(6,1);
% Front-facing long-range radar sensor at the center of the front bumper of the car.
sensors1 = radarDetectionGenerator('SensorIndex', 1, 'Height', 0.2, 'MaxRange', 174, ... 
    'SensorLocation', [egoCar.Wheelbase + egoCar.FrontOverhang, 0], 'FieldOfView', [20, 5]);

% Rear-facing long-range radar sensor at the center of the rear bumper of the car.
sensors2 = radarDetectionGenerator('SensorIndex', 2, 'Height', 0.2, 'Yaw', 180, ...
    'SensorLocation', [-egoCar.RearOverhang, 0], 'MaxRange', 174, 'FieldOfView', [20, 5]);

% Rear-left-facing short-range radar sensor at the left rear wheel well of the car.
sensors3 = radarDetectionGenerator('SensorIndex', 3, 'Height', 0.2, 'Yaw', 120, ...
    'SensorLocation', [0, egoCar.Width/2], 'MaxRange', 30, 'ReferenceRange', 50, ...
    'FieldOfView', [90, 5], 'AzimuthResolution', 10, 'RangeResolution', 1.25);

% Rear-right-facing short-range radar sensor at the right rear wheel well of the car.
sensors4 = radarDetectionGenerator('SensorIndex', 4, 'Height', 0.2, 'Yaw', -120, ...
    'SensorLocation', [0, -egoCar.Width/2], 'MaxRange', 30, 'ReferenceRange', 50, ...
    'FieldOfView', [90, 5], 'AzimuthResolution', 10, 'RangeResolution', 1.25);

% Front-left-facing short-range radar sensor at the left front wheel well of the car.
sensors5 = radarDetectionGenerator('SensorIndex', 5, 'Height', 0.2, 'Yaw', 60, ...
    'SensorLocation', [egoCar.Wheelbase, egoCar.Width/2], 'MaxRange', 30, ...
    'ReferenceRange', 50, 'FieldOfView', [90, 5], 'AzimuthResolution', 10, ...
    'RangeResolution', 1.25);

% Front-right-facing short-range radar sensor at the right front wheel well of the car.
sensors6 = radarDetectionGenerator('SensorIndex', 6, 'Height', 0.2, 'Yaw', -60, ...
    'SensorLocation', [egoCar.Wheelbase, -egoCar.Width/2], 'MaxRange', 30, ...
    'ReferenceRange', 50, 'FieldOfView', [90, 5], 'AzimuthResolution', 10, ...
    'RangeResolution', 1.25);

%% Create a Tracker
% Create a |<matlab:doc('multiObjectTracker') multiObjectTracker>| to track
% the vehicles that are close to the ego vehicle. The tracker uses the
% |initSimDemoFilter| supporting function to initialize a constant velocity
% linear Kalman filter that works with position and velocity.
% 
% Tracking is done in 2-D. Although the sensors return measurements in 3-D,
% the motion itself is confined to the horizontal plane, so there is no
% need to track the height.



%% TODO*
%Change the Tracker Parameters and explain the reasoning behind selecting
%the final values. You can find more about parameters here: https://www.mathworks.com/help/driving/ref/multiobjecttracker-system-object.html

tracker = multiObjectTracker('FilterInitializationFcn', @initSimDemoFilter, ...
    'AssignmentThreshold', 30, 'ConfirmationParameters', [4 5], 'NumCoastingUpdates', 5);
positionSelector = [1 0 0 0; 0 0 1 0]; % Position selector
velocitySelector = [0 1 0 0; 0 0 0 1]; % Velocity selector

% Create the display and return a handle to the bird's-eye plot
BEP = createDemoDisplay(egoCar, sensors);



%% Simulate the Scenario
% The following loop moves the vehicles, calls the sensor simulation, and
% performs the tracking.
% 
% Note that the scenario generation and sensor simulation can have
% different time steps. Specifying different time steps for the scenario
% and the sensors enables you to decouple the scenario simulation from the
% sensor simulation. This is useful for modeling actor motion with high
% accuracy independently from the sensor's measurement rate.
% 
% Another example is when the sensors have different update rates. Suppose
% one sensor provides updates every 20 milliseconds and another sensor
% provides updates every 50 milliseconds. You can specify the scenario with
% an update rate of 10 milliseconds and the sensors will provide their
% updates at the correct time.
% 
% In this example, the scenario generation has a time step of 0.01 second,
% while the sensors detect every 0.1 second. The sensors return a logical
% flag, |isValidTime|, that is true if the sensors generated detections.
% This flag is used to call the tracker only when there are detections.
% 
% Another important note is that the sensors can simulate multiple
% detections per target, in particular when the targets are very close to
% the radar sensors. Because the tracker assumes a single detection per
% target from each sensor, you must cluster the detections before the
% tracker processes them. This is done by the function |clusterDetections|.
% See the 'Supporting Functions' section.

toSnap = true;
while advance(scenario) && ishghandle(BEP.Parent)    
    % Get the scenario time
    time = scenario.SimulationTime;
    
    % Get the position of the other vehicle in ego vehicle coordinates
    ta = targetPoses(egoCar);
    
    % Simulate the sensors
    detections = ;
    isValidTime = false(1,6);
    for i = 1:6
        [sensorDets,numValidDets,isValidTime(i)] = sensorsi(ta, time);
        if numValidDets
            detections = [detections; sensorDets]; %#ok<AGROW>
        end
    end
    
    % Update the tracker if there are new detections
    if any(isValidTime)
        vehicleLength = sensors1.ActorProfiles.Length;
        detectionClusters = clusterDetections(detections, vehicleLength);
        confirmedTracks = updateTracks(tracker, detectionClusters, time);
        
        % Update bird's-eye plot
        updateBEP(BEP, egoCar, detections, confirmedTracks, positionSelector, velocitySelector);
    end
    
    % Snap a figure for the document when the car passes the ego vehicle
    if ta(1).Position(1) > 0 && toSnap
        toSnap = false;
        snapnow
    end
end

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