% Frequency of operation = 77GHz
% Max Range = 200m
% Range Resolution = 1 m
% Max Velocity = 100 m/s
%TODO : define the target's initial position and velocity. Note : Velocity remains contant
range = 110
vel = -20
max_range = 200
range_res = 1
max_vel = 100 % m/s
% *%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
% The sweep time can be computed based on the time needed for the signal to travel the unambiguous
% maximum range. In general, for an FMCW radar system, the sweep time should be at least
% 5 to 6 times the round trip time. This example uses a factor of 5.5.
B = c / (2*range_res)
Tchirp = 5.5 * 2 * (max_range/c)
slope = 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*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));
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) = range + (vel*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 ) );
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
%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.
signal_fft = fft(Mix, Nr);
% *%TODO* :
% Take the absolute value of FFT output
signal_fft = abs(signal_fft);
signal_fft = signal_fft ./ max(signal_fft); % Normalize
% *%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.
signal_fft = signal_fft(1 : Nr/2-1);
plotting the range
figure ('Name','Range from First FFT')
% *%TODO* :
% plot FFT output
plot(signal_fft);
axis ([0 180 0 1]);
title('Range from First FFT');
ylabel('Amplitude (Normalized)');
xlabel('Range [m]');
axis ([0 200 0 1]);
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. CFAR is applied to 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.
signal_fft2 = fft2(Mix,Nr,Nd);
% Taking just one side of signal from Range dimension.
signal_fft2 = signal_fft2(1:Nr/2,1:Nd);
signal_fft2 = fftshift (signal_fft2);
RDM = abs(signal_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);
title('Amplitude and Range From FFT2');
xlabel('Speed');
ylabel('Range');
zlabel('Amplitude');
CFAR implementation {#8}
%Slide Window through the complete Range Doppler Map
% *%TODO* :
%Select the number of Training Cells in both the dimensions.
n_train_cells = 10;
n_train_bands = 8;
% *%TODO* :
%Select the number of Guard Cells in both dimensions around the Cell under
%test (CUT) for accurate estimation
n_guard_cells = 4;
n_guard_bands = 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
RDM = RDM / max(RDM(:));
for row0 = n_train_cells + n_guard_cells + 1 : (Nr/2) - (n_train_cells + n_guard_cells)
for col0 = n_train_bands + n_guard_bands + 1 : (Nd) - (n_train_bands + n_guard_bands)
%Create a vector to store noise_level for each iteration on training cells
noise_level = zeros(1, 1);
for row1 = row0 - (n_train_cells + n_guard_cells) : row0 + (n_train_cells + n_guard_cells)
for col1 = col0 - (n_train_bands + n_guard_bands) : col0 + (n_train_bands + n_guard_bands)
if (abs(row0 - row1) > n_guard_cells || abs(col0 - col1) > n_guard_bands)
noise_level = noise_level + db2pow(RDM(row1, col1));
end
end
end
% Calculate threshold from noise average then add the offset
thresh = pow2db(noise_level / (2 * (n_train_bands + n_guard_bands + 1) * 2 * (n_train_cells + n_guard_cells + 1) - (n_guard_cells * n_guard_bands) - 1));
thresh = thresh + offset;
CUT = RDM(row1,col1);
if (CUT < thresh)
RDM(row0, col0) = 0;
else
RDM(row0, col0) = 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.
RDM(RDM~=0 & RDM~=1) = 0;
% *%TODO* :
%display the CFAR output using the Surf function like we did for Range
%Doppler Response output.
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');
view(315, 45);
