Radar_Target_Generation_and_Detection.m

clear all
clc;

%% Radar Specifications 
%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Frequency of operation = 77GHz
% Max Range = 200m
% Range Resolution = 1 m
% Max Velocity = 100 m/s
%%%%%%%%%%%%%%%%%%%%%%%%%%%

range_max = 200;        % given radar's max range
delta_r = 1;            % range resolution in meters
max_velocity = 100;
c = 3e8;                % speed of light in meters/sec

%% User Defined Range and Velocity of target
% *%TODO* :
% define the target's initial position and velocity. Note : Velocity
% remains contant

target_range = 110;
target_velocity = -20;

%% 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*delta_r;                % Bsweep calculation
Tchirp = 5.5*(range_max*2/c);   % 5.5 times the trip time for maximum range
slope = B/Tchirp;               % Slope of the FMCW

%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)); %range covered
td=zeros(1,length(t)); % time delay

%% 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) = target_range + target_velocity * 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));
    delay = t(i) - td(i); % time delayed version
    Rx (i)  = cos(2*pi*(fc*delay + slope * (delay^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.
Mix = reshape(Mix,[Nr,Nd]);

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

 % *%TODO* :
% Take the absolute value of FFT output
sig_fft = abs(sig_fft);

 % *%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.
sig_fft= sig_fft(1:Nr/2 - 1);

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

 % *%TODO* :
 % plot FFT output
plot(sig_fft);
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 = 8;
Td = 6;

% *%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.

num_cells = 2*(Td+Gd+1)*2*(Tr+Gr+1)- (Gr*Gd-1);

RDM = RDM/max(max(RDM));
   % Use RDM[x,y] as the matrix from the output of 2D FFT for implementing
   % CFAR

    % Slide the cell under test across the complete matrix.
    % Make sure the CUT has margin for Training and Guard cells from the edges.
    % For every iteration sum the signal level within all the training cells.
    % To sum convert the value from logarithmic to linear using db2pow function.
    for i = (Tr + Gr + 1):((Nr / 2) - (Gr + Tr))
        for j = (Td + Gd + 1):(Nd - (Gd + Td))
            for p = (i - (Tr + Gr)):(i + (Tr + Gr))
                for q = (j - (Td + Gd)):(j + (Td + Gd))
                    if (abs(i - p) > Gr || abs(j - q) > Gd)
                        noise_level = noise_level + db2pow(RDM(p,q));
                    end
                end
            end
        
        % Average the summed values for all of the training cells used.
        % After averaging convert it back to logarithmic using pow2db.
        threshold = pow2db(noise_level/num_cells);

        % Further add the offset to it to determine the threshold.
        threshold = threshold + 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.
        CUT = RDM(i,j);
        if (CUT > threshold)
            RDM(i,j) = 1;
        else
            RDM(i,j) = 0;
        end
            
        % reset noise level value for next iteration
        noise_level = zeros(1,1);
        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. 

% To keep the map size same as it was before CFAR, equate all the non-thresholded cells 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,surf(doppler_axis,range_axis,RDM);
colorbar;