project.m

clc;
clear all;
close all;
%% Radar Specifications 
%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Frequency of operation = 77GHz
% Max Range = 200m
% Range Resolution = 1 m
% Max Velocity = 100 m/s
%%%%%%%%%%%%%%%%%%%%%%%%%%%

%speed of light = 3e8
%% User Defined Range and Velocity of target
% *%TODO* :
% define the target's initial position and velocity. Note : Velocity
% remains contant
 
R_0 = 50; % Target Initial range

V = -25;   % Target velocity


%% 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.


%Operating carrier frequency of Radar 
fc= 77e9;             %carrier freq

dr = 1; % Range Resolution

R_max = 200; % Maximum range radar can detect

V_max = 100; % Maximum velocity radar can detect

V_dr = 3; % Velocity Resolution

c = 3e8; % speed of light


B = c/2*dr; %bandwidth or range of frequency in FMCW

chirp_time = 5.5*R_max*2/c; %Chirp time

slope = B/chirp_time;




%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*chirp_time,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. 
    
    range = R_0 +t(i)*V;
        % *%TODO* :
        %For each time sample we need update the transmitted and
        %received signal. 
    td = 2*range/c;
    t_new = t(i) - td;

    Tx(i) = cos( 2*pi*( fc*t(i) + (0.5*slope*t(i)^2 ) ) );
    Rx(i)  = cos( 2*pi*( fc*t_new + (0.5*slope*t_new^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.

range_fft = reshape(Mix, [Nr,Nd]);

 % *%TODO* :
%run the FFT on the beat signal along the range bins dimension (Nr) and
%normalize.

sigfft = fft(range_fft);

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


sig = sigfft(1: length(t)/2 + 1); % take only one half of output

 % *%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.


%plotting the range
figure ('Name','Range from First FFT')
f = length(t)*(0:length(t)/2)/length(t);
plot(f,sig);
%subplot(2,1,1)

 % *%TODO* :
 % plot FFT output 

 
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) ;
maxV = max(max(RDM));
RDM = RDM/maxV;

%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 and Speed From FFT2')
surf(doppler_axis,range_axis,RDM);
%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 = 12;
Td = 10;
% *%TODO* :
%Select the number of Guard Cells in both dimensions around the Cell under 
%test (CUT) for accurate estimation

Gr = 6; 
Gd = 6;
% *%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


cfar_thresh = [];
signal_cfar = [];

for i = Tr+Gr+1 : (Nr/2)-(Gr+Tr)
    for j = Td+Gd+1 : Nd-(Gd+Td)
        
        % init noise level
        noise_level = zeros(1,1);
        
        % Calculate noise SUM in the area around CUT
        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
        
        % Calculate threshould from noise average then add the offset
        th = pow2db(noise_level/(2*(Td+Gd+1)*2*(Tr+Gr+1)-(Gr*Gd)-1));
        th = th + offset;
        CUT = RDM(i,j);
        
        if (CUT > th)
            RDM(i,j) = 1;
            %fprintf ("p= %d, q= %d, CUT= %f, th= %f\n", p, q, CUT, th);
        else
            RDM(i,j) = 0;
        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(union(1:(Tr+Gr),end-(Tr+Gr-1):end),:) = 0;  % Rows
RDM(:,union(1:(Td+Gd),end-(Td+Gd-1):end)) = 0;  % Columns





% *%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;