极简 FMCW 毫米波雷达 Matlab 仿真

    %% Radar Specifications 
    %%%%%%%%%%%%%%%%%%%%%%%%%%%
    % Frequency of operation = 77GHz
    % Max Range = 200m
    % Range Resolution = 1 m
    % Max Velocity = 100 m/s
    %%%%%%%%%%%%%%%%%%%%%%%%%%%
    
    c = 3e8;
    range = 110;
    vel = -20;
    max_range = 200;
    range_res = 1;
    max_vel = 100; 
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    %% FMCW Waveform Generation
    
    % *%TODO* :
    %Design the FMCW waveform by giving the specs of each of its parameters.
    % Calculate the Bandwidth (B), Chirp Time (Tchirp) and Slope (slope) of the FMCW
    % chirp using the requirements above.
    B = c / (2*range_res);
    Tchirp = 5.5 * 2 * (max_range/c) ; 
    slope = B/Tchirp;
    
    
      
      
      
      
      
      
      
      
      
    
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    %Operating carrier frequency of Radar 
    fc= 77e9;             %carrier freq
                                                          
    %The number of chirps in one sequence. Its ideal to have 2^ value for the ease of running the FFT
    %for Doppler Estimation. 
    Nd=128;                   % #of doppler cells OR #of sent periods % number of chirps
    
    %The number of samples on each chirp. 
    Nr=2^15;                  %for length of time OR # of range cells
    
    
      
      
      
      
      
      
      
      
      
    
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    % Timestamp for running the displacement scenario for every sample on each
    % chirp
    t=linspace(0,Nd*Tchirp,Nr*Nd); %total time for samples
    
    %Creating the vectors for Tx, Rx and Mix based on the total samples input.
    Tx=zeros(1,length(t)); %transmitted signal
    Rx=zeros(1,length(t)); %received signal
    Mix = zeros(1,length(t)); %beat signal
    
    %Similar vectors for range_covered and time delay.
    r_t=zeros(1,length(t));
    td=zeros(1,length(t));
    
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    %% Signal generation and Moving Target simulation
    % Running the radar scenario over the time. 
    
    for i=1:length(t)         
      % *%TODO* 
      %For each time stamp update the Range of the Target for constant velocity. 
      r_t(i) = range + (vel*t(i));
      td(i) = (2 * r_t(i)) / c;
    
      % *%TODO* :
      %For each time sample we need update the transmitted and
      %received signal. 
    
      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 ) );
    
      % *%TODO* :
      %Now by mixing the Transmit and Receive generate the beat signal
      %This is done by element wise matrix multiplication of Transmit and
      %Receiver Signal
    
      Mix(i) = Tx(i) .* Rx(i);
    end
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    % 绘制一个chrip的Tx信号的频率域结果
    signal_fft = fft(Tx, Nr);
    N = length(signal_fft);
    Fs=Nr/Tchirp;
    f = (0:N-1)*(Fs/N);
    plot(f(1:N/2), abs(signal_fft(1:N/2)));
    title('Single-Sided Amplitude Spectrum');
    xlabel('Frequency (Hz)');
    ylabel('|FFT|');
    
    % 绘制混频后的信号的第一个脉冲
    plot(t(1:1*Nr), Mix(1:1*Nr));
    title('Beat Signal');
    xlabel('Time');
    ylabel('Amplitude');
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    %% RANGE MEASUREMENT
    
     % *%TODO* :
    %reshape the vector into Nr*Nd array. Nr and Nd here would also define the size of
    %Range and Doppler FFT respectively.
    Mix = reshape(Mix, [Nr, Nd]);
    MixT = Mix';
    dim = 2;
     
    % fft运算并输出频谱图,这里只打印4行
    signal_fft = fft(MixT, Nr, dim);
    
    P2 = abs(signal_fft/Nr);
    P1 = P2(:,1:Nr/2+1);
    P1(:,2:end-1) = 2*P1(:,2:end-1);
    
    % figure;
    % for i=1:4
    %     subplot(4,1,i)
    %     plot(0:(Fs/Nr):(Fs/2-Fs/Nr),P1(i,1:Nr/2))
    %     title("Row " + num2str(i) + " in the Frequency Domain")
    % end
    
    % 计算距离并直接体现在横坐标上,这里只打印4行
    rowRange = (0:(Fs/Nr):(Fs/2-Fs/Nr))*c/slope/2;
    
    figure;
    for i=1:4
    subplot(4,1,i)
    plot(rowRange,P1(i,1:Nr/2))
    xlim([0,200]);
    xlabel('Range [m]');
    title("Row " + num2str(i) + " Range")
    end
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    %% RANGE DOPPLER RESPONSE
    % The 2D FFT implementation is already provided here. This will run a 2DFFT
    % on the mixed signal (beat signal) output and generate a range doppler
    % map.You will implement CFAR on the generated RDM
    
    % Range Doppler Map Generation.
    % The output of the 2D FFT is an image that has reponse in the range and
    % doppler FFT bins. So, it is important to convert the axis from bin sizes
    % to range and doppler based on their Max values.
    
    Mix=reshape(Mix,[Nr,Nd]);
    
    % 2D FFT using the FFT size for both dimensions.
    signal_fft2 = fft2(Mix,Nr,Nd);
    
    % Taking just one side of signal from Range dimension.
    signal_fft2 = signal_fft2(1:Nr/2,1:Nd);
    signal_fft2 = fftshift (signal_fft2);
    
    RDM = abs(signal_fft2);
    RDM = 10*log10(RDM) ; % 转换为分贝单位
    
    %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,surf(doppler_axis,range_axis,RDM,'EdgeColor', 'none');
    title('Amplitude and Range From FFT2');
    xlabel('Speed');
    ylabel('Range');
    zlabel('Amplitude');
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    %% CFAR implementation
    
    %Slide Window through the complete Range Doppler Map
    
    % *%TODO* :
    %Select the number of Training Cells in both the dimensions.
    n_train_cells = 10;
    n_train_bands = 8;
    
    % *%TODO* :
    %Select the number of Guard Cells in both dimensions around the Cell under 
    %test (CUT) for accurate estimation
    n_guard_cells = 4;
    n_guard_bands = 4;
    
    % *%TODO* :
    % offset the threshold by SNR value in dB
    offset = 1.4;
    
    % *%TODO* :
    %Create a vector to store noise_level for each iteration on training cells
    noise_level = zeros(1,1);
    
    
    % *%TODO* :
    %design a loop such that it slides the CUT across range doppler map by
    %giving margins at the edges for Training and Guard Cells.
    %For every iteration sum the signal level within all the training
    %cells. To sum convert the value from logarithmic to linear using db2pow
    %function. Average the summed values for all of the training%cells used. After averaging convert it back to logarithimic using pow2db.
    %Further add the offset to it to determine the threshold. Next, compare the
    %signal under CUT with this threshold. If the CUT level > threshold assign
    %it a value of 1, else equate it to 0.
    
    
       % Use RDM[x,y] as the matrix from the output of 2D FFT for implementing
       % CFAR
    
    RDM = RDM / max(RDM(:));
    
    
    for row0 = n_train_cells + n_guard_cells + 1 : (Nr/2) - (n_train_cells + n_guard_cells)
      for col0 = n_train_bands + n_guard_bands + 1 : (Nd) - (n_train_bands + n_guard_bands)
    %Create a vector to store noise_level for each iteration on training cells
    noise_level = zeros(1, 1);
    
    for row1 = row0 - (n_train_cells + n_guard_cells) : row0 + (n_train_cells + n_guard_cells)
      for col1 = col0 - (n_train_bands + n_guard_bands) : col0 + (n_train_bands + n_guard_bands)
        if (abs(row0 - row1) > n_guard_cells || abs(col0 - col1) > n_guard_bands)
          noise_level = noise_level + db2pow(RDM(row1, col1));
        end
      end
    end
    
    % Calculate threshold from noise average then add the offset
    thresh = pow2db(noise_level / (2 * (n_train_bands + n_guard_bands + 1) * 2 * (n_train_cells + n_guard_cells + 1) - (n_guard_cells * n_guard_bands) - 1));
    thresh = thresh + offset;
    
    CUT = RDM(row1,col1);
    
    if (CUT < thresh)
      RDM(row0, col0) = 0;
    else
      RDM(row0, col0) = 1;
    end
    
      end
    end
    
    
    % *%TODO* :
    % The process above will generate a thresholded block, which is smaller 
    %than the Range Doppler Map as the CUT cannot be located at the edges of
    %matrix. Hence,few cells will not be thresholded. To keep the map size same
    % set those values to 0. 
    
    RDM(RDM~=0 & RDM~=1) = 0;
    
    % *%TODO* :
    %display the CFAR output using the Surf function like we did for Range
    %Doppler Response output.
    figure('Name', 'CA-CFAR Filtered RDM')
    surf(doppler_axis, range_axis, RDM);
    %colorbar;
    title( 'CA-CFAR Filtered RDM surface plot');
    xlabel('Speed');
    ylabel('Range');
    zlabel('Normalized Amplitude');
    view(315, 45);
    
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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