Radar target generation and detection

Workflow :

1. Range 1D-FFT for range detection

2. Doppler 2D-FFT for velocity and range detection

3. CFAR clutter removal technique and thresholding

4. Final Project : apply all the above with 2-D CFAR

Directory contents :

1. Range_detection/ : FFT1D.m , range.m (range detection)

1-D FFT for range detection

2. Doppler_velocity_detection : FFT2D.m , dopplerVelocity.m

2-D FFT for doppler velocity and range detection plot

3. Clutter_and_thresholding : CFAR1D.m (only 1-D CFAR here, 2-D CFAR in the project.m file)

1-D CFAR with self adjusting threshold based on noise and object density.

2D CFAR process

• Loop over elements of RDM array each iteration selecting one cell to be the CUT (Cell Under Test)
  for i = Tr+Gr+1 : (Nr/2)-(Gr+Tr)
for j = Td+Gd+1 : Nd-(Gd+Td)
• For each iteration loop over the training cells excluding the guarding cells to sum their values
  for p = i-(Tr+Gr) : i+(Tr+Gr)
for q = j-(Td+Gd) : j+(Td+Gd)
• Calculate the average of the noise value
  noise_level = noise_level + db2pow(RDM(p,q));
• Convert using pow2db
  th = pow2db(noise_level/(2*(Td+Gd+1)*2*(Tr+Gr+1)-(Gr*Gd)-1));
• Add the offset value
• If the CUT is greater then the threshold replace it by 1, else 0

Training, Guard cells and offset

• Tr = 12, Td = 10 For Range and Doppler Training Cells.
• Gr = 6, Gd = 6 For Range and Doppler Guard Cells.
• offset = 1.4 offset value.

suppress the non-thresholded cells at the edges

sclicing the output such that we have the surrounding rows and columns depending on the Training cells for both range and doppler.
RDM(union(1:(Tr+Gr),end-(Tr+Gr-1):end),:) = 0; % Rows
RDM(:,union(1:(Td+Gd),end-(Td+Gd-1):end)) = 0; % Columns

Final Results:

final output for a target at 50m moving at -25 m/s relative velocity

Range detection 50m

Range and velocity from 2D-FFT

2-D CFAR