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
%%%%%%%%%%%%%%%%%%%%%%%%%%%

%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 = 110; % Initial position of the target
v = -20; % Initial velocity of the target (Receding)
 
%% 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_frequency = 77e9;
MaxRange = 200;
RangeResolution = 1;
MaxVelocity = 100;
c = 3e8;
Sweep_time = 5.5*2*MaxRange / c;

Bandwidth = c / 2*RangeResolution;

Slope = Bandwidth / Sweep_time;

%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*Sweep_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. 
    
    r_t(i) = R + v*t(i);
    td(i) = 2*r_t(i) / c; % Time delay
    
    % *%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

%% RANGE MEASUREMENT


 % *%TODO* :
%reshape the Mix signal vector into Nr*Nd array. Nr and Nd here would also define the size of
%Range and Doppler FFT respectively.
% Nd: Number of chirps
% Nr: Number of samples in each chirp
MixReshape = reshape(Mix, [Nr, Nd]);
 % *%TODO* :
%run the FFT on the beat signal along the range bins dimension (Nr) and
%normalize.
MixFFT = fft(MixReshape, [], 1) / Nr;
 % *%TODO* :
% Take the absolute value of FFT output
MixFFTabs = abs(MixFFT);
 % *%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.
SingleSidedSpectrum = MixFFTabs(1:Nr/2);
SingleSidedSpectrum(2:end-1) = 2*SingleSidedSpectrum(2:end-1);

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

 % *%TODO* :
 % plot FFT output 
%f = Nr*(0:(Nr/2)) / Nr;
plot(SingleSidedSpectrum);
title('Single-Sided Amplitude Spectrum of SingleSidedSpectrum')

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); % surf creates a 3D surface plot

%% CFAR implementation

%Slide Window through the complete Range Doppler Map

% *%TODO* :
%Select the number of Training Cells in both the dimensions.

Tr = 12; % In range dimension
Td = 6; % In doppler dimension

% *%TODO* :
%Select the number of Guard Cells in both dimensions around the Cell under 
%test (CUT) for accurate estimation
Gr = 4;
Gd = 3;

% *%TODO* :
% offset the threshold by SNR value in dB (Adding room above noise
% threshold)
offset = 1.2;

% *%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
% Normalize the matrix
RDM = RDM / max(max(RDM));
for i = Tr + Gr + 1 : Nr/2 - (Gr + Tr)
     for j = Td + Gd + 1 : Nd - (Gd + Td)
         noise_level = zeros(1,1);
         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
         threshold = pow2db(noise_level/(2*(Td+Gd+1)*2*(Tr+Gr+1)-(Gr*Gd)-1));
         threshold = threshold + offset;
         
         CUT = RDM(i,j);
         if CUT < threshold
             RDM(i, j) = 0;
         else
             RDM(i, j) = 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.
% REFERENCE: https://knowledge.udacity.com/questions/588592
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;