Example code for radar detection with matlab
Using the given system requirements, design
a FMCW waveform. Define its Bandwidth (B), chirp time (Tchirp) and slope of the chirp.
I define the targetObject at the following position and velocity:
% define the target's initial position and velocity. Note : Velocity
% remains contant
targetPos = 77;
targetSpeed = 30;Simulate Target movement and calculate the beat or mixed signal for every timestamp.
FFT for target at 77 meters
Implement the Range FFT on the Beat or Mixed Signal and plot the result.
Plot show maximum of amplitude normalized over speed and range. Noisy data around peak gets removed with CFAR algorithm.
Implement the 2D CFAR process on the output of 2D FFT operation, i.e the Range Doppler Map. Remove the noise from our signal peak do this by applying the CFAR Algorithm. Shift a matrix consisting of guard and train cells through the signal matrix and check if cellUnder Test(look at image) cell is lower or higher then the threshold. The threshold gets calculated by calculating the signal power (db2pow) of all the trainingCells. If higher set to 1, if lower set to 0. I have to admit the nested for loop here is not good practice costly in performance.
%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.
% Determine the number of Training cells for each dimension. Similarly, pick the number of guard cells.
% 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.
% Average the summed values for all of the training cells used. After averaging convert it back to logarithmic using pow2db.
% Further add the offset to it to determine the threshold.
% 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.
for a = tR+Gr+1 : (size(Mix,1)/2)-(tR+Gr)
for b = tC+Gd+1 : (size(Mix,2))-(tC+Gd)
%Create a vector to store noise_level for each iteration on training cells
noiseLevel = zeros(1,1);
%Step through each of bins and the surroundings of the CUT
for c = a - (tR+Gr) : a + (tR+Gr)
for d = b-(tC + Gd) : b + (tC + Gd)
%Exclude the Guard cells and CUT cells
if (abs(a - c) > Gr || abs(b - d) > Gd)
%Convert db to power
noiseLevel = noiseLevel + db2pow(RDM(c,d)); %requires SignalToolbox
end
end
end
%Calculate threshold from noise average then add the offset
threshold = pow2db(noiseLevel/(2*(tC+Gd+1)*2*(tR+Gr+1)-(Gr*Gd)-1));
%Add the SNR to the threshold
threshold = threshold + offSet;
%Measure the signal in Cell Under Test(CUT) and compare against
CUT = RDM(a,b);
if (CUT < threshold)
RDM(a,b) = 0;
else
RDM(a,b) = 1;
end
end
end
RDM(RDM~=0 & RDM~=1) = 0;Filtered Signal:
