SFND_Radar_Target_Detection-master_雷达目标检测脉冲压缩SFND_

# Radar Target Generation and Detection Project for Udacity's Sensor Fusion Engineer Nanodegree Program If you want to find more details, please check my blog: [Radar target generation and detection-Hardware](https://blog.csdn.net/williamhyin/article/details/105414455), [Radar target generation and detection-Software](https://blog.csdn.net/williamhyin/article/details/105414541) ### Project Goal - Configure the FMCW waveform based on the system requirements. - Define the range and velocity of target and simulate its displacement. - For the same simulation loop process the transmit and receive signal to determine the beat signal - Perform Range FFT on the received signal to determine the Range - Towards the end, perform the CFAR processing on the output of 2nd FFT to display the target. ### Overview  ### Steps 1. Radar specifications ```matlab %% 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 ``` 2. Target specifications ```matlab %% 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; ``` 3. FMCW Waveform Generation In this project, we will designing a Radar based on the given system requirements (above). Max Range and Range Resolution will be considered here for waveform design. ```matlab % *%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; ``` Then, we need simulate the signal propagation and move target scenario.  ```matlab %% 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 ``` 4. Range measurement The 1st FFT output for the target located at 100 meters  5. Range and Doppler measurement 2st FFT will generate a Range Doppler Map as seen in the image below and it will be given by variable ‘RDM’.  6. CFAR implementation The 2D CFAR is similar to 1D CFAR, but is implemented in both dimensions of the range doppler block. The 2D CA-CFAR implementation involves the training cells occupying the cells surrounding the cell under test with a guard grid in between to prevent the impact of a target signal on the noise estimate.  1. Select the number of Training Cells and Guard Cells in both the dimensions and set offset of threshold ```matlab % *%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; ``` 2. Slide Window through the complete Range Doppler Map ```matlab % *%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; ``` The output of the 2D CFAR process，a peak and spread centered at 100m in range direction and 30 m/s in the doppler direction.