radar_target_generation_and_detection.m

clear all
close all;
clc;

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

max_range=200;
c = 3e8;
range_resolution= 1;

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

%% User Defined Range and Velocity of target
% *%TODO* :
% define the target's initial position and velocity. Note : Velocity
% remains contant
target_pos=100;
target_speed=30;


%% 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_sweep = c/(2*range_resolution); %Calculate the Bandwidth (B)

T_chirp = 5.5*2*max_range/c;

slope=B_sweep/T_chirp;

                                                          
%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*T_chirp,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) = target_pos+(target_speed*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 vector into Nr*Nd array. Nr and Nd here would also define the size of
%Range and Doppler FFT respectively.

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

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

sig_fft1=fft(Mix_reshape,Nr)./Nr;

 % *%TODO* :
% Take the absolute value of FFT output

sig_fft1=abs(sig_fft1);

 % *%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_fft1=sig_fft1(1:Nr/2);


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

 % *%TODO* :
 % plot FFT output 

plot(sig_fft1);
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

off_set=1.4;


% *%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(max(RDM)); % Normalizing

% *%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. 

%Slide the cell under test across the complete martix,to note: start point
%Tr+Td+1 and Td+Gd+1
for i = Tr+Gr+1:(Nr/2)-(Tr+Gr)
    for j = Td+Gd+1:(Nd)-(Td+Gd)
        %Create a vector to store noise_level for each iteration on training cells
        noise_level = zeros(1,1);
        %Step through each of bins and the surroundings of the CUT
        for p = i-(Tr+Gr) : i+(Tr+Gr)
            for q = j-(Td+Gd) : j+(Td+Gd)
                %Exclude the Guard cells and CUT cells
                if (abs(i-p) > Gr || abs(j-q) > Gd)
                    %Convert db to power
                    noise_level = noise_level + db2pow(RDM(p,q));
                end
            end
        end
        
        %Calculate threshould from noise average then add the offset
        threshold = pow2db(noise_level/(2*(Td+Gd+1)*2*(Tr+Gr+1)-(Gr*Gd)-1));
        %Add the SNR to the threshold
        threshold = threshold + off_set;
        %Measure the signal in Cell Under Test(CUT) and compare against
        CUT = RDM(i,j);
        
        if (CUT < threshold)
            RDM(i,j) = 0;
        else
            RDM(i,j) = 1;
        end
        
    end
end

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,'replace this with output');
% colorbar;
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');