毕业设计&课设-利用matlab对雷达探测距离、速度进行仿真，在仿真范围内分别采用了_FFT和doppler_FFT。.zip

# SFND Radar Target Generation and Detection ## [Rubric](https://review.udacity.com/#!/rubrics/2548/view) Points --- #### 1. FMCW Waveform Design Using the given system requirements, design a FMCW waveform. Find its Bandwidth (B), chirp time (Tchirp) and slope of the chirp. ```Matlab %% Radar Specifications %%%%%%%%%%%%%%%%%%%%%%%%%%% % Frequency of operation = 77GHz % Max Range = 200m % Range Resolution = 1 m % Max Velocity = 100 m/s %%%%%%%%%%%%%%%%%%%%%%%%%%% fc= 77e9; %carrier freq d_res = 1; c = 3*10^8; RMax = 200; Bsweep = c/(2*d_res); %Bandwidth Tchirp = 5.5*2*RMax/c; %chirp time alpha = Bsweep/Tchirp; %slope of chirps ``` #### 2. Simulation Loop Simulate Target movement and calculate the beat or mixed signal for every timestamp. ```Matlab d0 = 80; %initial position v0 = -50; %initial velocity Nd=128; % number of chirps Nr=1024; % number of samples of one chirp t=linspace(0,Nd*Tchirp,Nr*Nd); %total time for samples Tx=zeros(1,length(t)); %transmitted signal Rx=zeros(1,length(t)); %received signal Mix = zeros(1,length(t)); %beat signal r_t=zeros(1,length(t)); td=zeros(1,length(t)); disp(length(t)) for i = 1:length(t) r_t(i) = d0 + v0*t(i); td(i) = 2*r_t(i)/c; Tx(i) = cos(2*pi*(fc*t(i) + slope*t(i)^2/2)); Rx(i) = cos(2*pi*(fc*(t(i) - td(i)) + slope*(t(i) - td(i))^2/2)); Mix(i) = Tx(i) * Rx(i); end ``` #### 3. Range FFT (1st FFT) Implement the Range FFT on the Beat or Mixed Signal and plot the result. ```Matlab sig_fft = fft(Mix,Nr)./Nr; sig_fft = abs(sig_fft); % Take the absolute value of FFT output % Output of FFT is double sided signal, but we are interested in only one side of the spectrum.Hence we throw out half of the samples. sig_fft = sig_fft(1:(Nr/2)); %plotting the range figure ('Name','Range from First FFT') plot(sig_fft); grid minor % plot FFT output axis ([0 200 0 1]); xlabel('measured range'); ```  #### 4. doppler FFT (2st FFT) ```Matlab Mix=reshape(Mix,[Nr,Nd]); % 2D FFT using the FFT size for both dimensions. sig_fft2 = fft2(Mix,Nr,Nd); % Taking just one side of signal from Range dimension. sig_fft2 = sig_fft2(1:Nr/2,1:Nd); sig_fft2 = fftshift (sig_fft2); RDM = abs(sig_fft2); RDM = 10*log10(RDM) ; %use the surf function to plot the output of 2DFFT and to show axis in both dimensions doppler_axis = linspace(-100,100,Nd); range_axis = linspace(-200,200,Nr/2)*((Nr/2)/400); figure('Name','Range Doppler Map'),surf(doppler_axis,range_axis,RDM); ```  #### 5. 2D CFAR Implement the 2D CFAR process on the output of 2D FFT operation, i.e the Range Doppler Map. Determine the number of Training cells for each dimension. Similarly, pick the number of guard cells. ```Matlab Tcr = 10; %the number of Training Cells in both the dimensions. Tcd = 4; Gcr = 5; %the number of Guard Cells in both dimensions around the Gcd = 2; %Cell under test (CUT) for accurate estimation offset = 1.4; % offset the threshold by SNR value in dB ``` Create a vector to store noise_level for each iteration on training cells，and get the training Cells Num. ```Matlab noise_level = zeros(Nr/2-2*(Tcd+Gcd),Nd-2*(Tcr+Gcr)); gridSize = (2*Tcr+2*Gcr+1)*(2*Tcd+2*Gcd+1); trainingCellsNum = gridSize-(2*Gcr+1)*(2*Gcd+1); CFAR_sig = zeros(size(RDM)); % ``` Slide the cell under test across the complete matrix. Make sure the CUT has margin for Training and Guard cells from the edges. ```Matlab for i = 1:(Nr/2-(2*Gr+2*Tr+1)) for j = 1:(Nd-(2*Gd+2*Td+1)) ... end end ``` For every iteration, convert the value from logarithmic to linear using db2pow function, then sum the signal level within all the training cells, and get the mean value. then convert it back to db using pow2db. ```Matlab trainingCellsPatch = db2pow(RDM(i:i+2*(Tcd+Gcd),j:j+2*(Gcr+Tcr))); trainingCellsPatch(Tcd+1:end-Tcd,Tcr+1:end-Tcr) = 0; noise_level(i,j) = pow2db(sum(sum(trainingCellsPatch))/trainingCellsNum); ``` Add the offset to it to determine the threshold. ```Matlab threshold = pow2db(sum_train/Tcell)*offset; ``` Next, compare the signal under CUT against this threshold. If the CUT level > threshold assign it a value of 1, else equate it to 0. ```Matlab if RDM(i+(Tcd+Gcd),j+(Tcd+Gcr))>sigThresh CFAR_sig(i+(Tcd+Gcd),j+(Tcd+Gcr)) = 1; else CFAR_sig(i+(Tcd+Gcd),j+(Tcd+Gcr)) = 0; end ``` Training, Guard cells and offset are selected by increasing and decreasing to match the image shared in walkthrough.