radar_target_generation_and_detection.m

clear all;
clc;
close all;

%% Radar Specifications 
%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Frequency of operation = 77GHz
% Max Range = 200m
% Range Resolution = 1 m
% Max Velocity = 100 m/s
%%%%%%%%%%%%%%%%%%%%%%%%%%%
rMax = 200.0;
dRes = 1.0;
c = 3e8;
vMax = 100.0;


%% User Defined Range and Velocity of target
% *%TODO* :
% define the target's initial position and velocity. Note : Velocity
% remains contant
targetRange = 150;
targetVel = -10.0;


%% 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.

% Calculate the Bandwidth (B)
B = c/(2*dRes);
% Calculate Chirp Time (Tchirp)
Tchirp = 5.5 * 2.0 * rMax /c;
% Calculate(slope) of the FMCW
fmcwSlope = B/Tchirp

%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=1024;                  %for length of time OR # of range cells

% 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));


%% 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. 
    td(i) = targetRange + (targetVel * t(i));
    r_t(i) = 2.0 * td(i)/c;
    
    % *%TODO* :
    %For each time sample we need update the transmitted and
    %received signal. 
    Tx(i) = cos(2 * pi * (fc*t(i) + fmcwSlope * t(i)^2 / 2));
    Rx (i)  =cos(2 * pi * (fc*(t(i) - r_t(i)) + fmcwSlope * (t(i) - r_t(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

%% 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.
fb = reshape(Mix, [Nr, Nd]);

 % *%TODO* :
%run the FFT on the beat signal along the range bins dimension (Nr) and
%normalize.
Y=fft(fb)/Nr;

 % *%TODO* :
% Take the absolute value of FFT output
P2 = abs(Y);
 % *%TODO* :
% 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.
P1 = P2(1:Nr/2);

%plotting the range
figure ('Name','Range from First FFT')
subplot(2,1,1)

 % *%TODO* :
 % plot FFT output 
 f = 0 : length(P1) - 1;
 plot(f, P1);
 grid on
 
axis ([0 200 0 1]);



%% 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.
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,surf(doppler_axis,range_axis,RDM);

%% CFAR implementation

%Slide Window through the complete Range Doppler Map

% *%TODO* :
%Select the number of Training Cells in both the dimensions.
Tr = 10;
Td = 8;
% *%TODO* :
%Select the number of Guard Cells in both dimensions around the Cell under 
%test (CUT) for accurate estimation
Gr = 4;
Gd = 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

%Determine the number of Training cells for each dimension.
%Similarly, pick the number of guard cells.
grid_size = 2*(Td+Gd+1)*2*(Tr+Gr+1);
train_cells = grid_size - (2*Gr+1)*(2*Gd+1);
signals = zeros(size(RDM));
[rangeSize, dopplerSize] = size(RDM);

for i = (Tr + Gr + 1) : (rangeSize - (Tr + Gr))
    for j = (Td + Gd + 1) : (dopplerSize - (Td + Gd))
        
        %vector to save each iteration
        noise_level = zeros(1,1);
        %For every iteration sum the signal level within all the training cells. 
        for k = i - (Tr + Gr) : i + Tr + Gr
            for l = j - (Td + Gd) : j + Td + Gd
                if (abs(i-k)>Gr || abs(j-l)>Gd)
                    % To sum convert the value from logarithmic to linear using
                    % db2pow function.
                    noise_level = noise_level + db2pow(RDM(k,l));
                end
            end
        end
        
        % Average the summed values for all of the training cells used.
        % After averaging convert it back to logarithimic using pow2db.
        threshold = pow2db(noise_level / train_cells);
    
        %Further add the offset to it to determine the threshold.
        threshold = threshold * offset;
        
        % 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.
        CUT = RDM(i,j);
        if CUT > threshold
            signals(i,j) = 1;
        end
    end
end


% *%TODO* :
%display the CFAR output using the Surf function like we did for Range
%Doppler Response output.
figure,surf(doppler_axis,range_axis,signals);
colorbar;