CCS/AWR1642BOOST: Matlab code for upto 2DFFT ( velocity rangle profile)

Tool/software: Code Composer Studio

Dear ,

I am using mmWave studio (with Awr1642 and DCA1000) to capture raw data and analyse it. Once I run the DataCaptureDemo, and the PostProc worked well, I got an adc_data.bin which is analysed by the PostProc. Is there any way to analyse this .bin file using a kind of script for angle estimation (detection and direction estimation) . By using below Matlab code I am able to process the data up to Range velocity profile but want to estimate angle of object . So Requesting you for provide to small script for estimating angle from the 2DFFT matrix/ range velocity profile.

% you provide the path and filename
fname = 'adc_data.bin'; % radar studio 
%fname = 'C:\Program Files (x86)\Texas Instruments\High Speed Data Converter Pro\ADC Temp.bin'; % tsw1400 hsdPro 

% enter chirp parameters here, you can modify the default, and select or modify the Config set (Configs A-G are predefined)
Config = 'F';

% default setting, if Config is outside A - G
slope_Hz_per_sec = 67e12;% 
sampling_rate_Sps = 30.0e6;
n_adc_samples = 256;
n_chirps = 128;
n_adc_bits = 16; %%%%%%%% Change this to 12/16 based on what is ADC bit set in device %%%%
n_rx = 4;
n_tx = 1;
isreal = 1; 

% start of code
lightSpeed_meters_per_sec = 3e8;
norm_factor = (sqrt(2)/(2^(n_adc_bits-1)-1));

if Config == 'A'
n_tx = 1; 
n_rx = 4; 
n_adc_samples = 100; 
sampling_rate_Sps = 10e6;
slope_Hz_per_sec = 10e12;
n_chirps = 128;
isreal = 0; 
elseif Config == 'B'
n_tx = 1; 
n_rx = 4; 
sampling_rate_Sps = 20e6;
n_adc_samples = 400; 
slope_Hz_per_sec = 10e12;
n_chirps = 128;
isreal = 1;
elseif Config == 'C'
n_tx = 1; 
n_rx = 4; 
sampling_rate_Sps = 30e6;
n_adc_samples = 900; 
slope_Hz_per_sec = 10e12;
n_chirps = 128;
isreal = 1;
elseif Config == 'D'
n_tx = 2; %1,2
n_rx = 4; 
sampling_rate_Sps = 10e6;
n_adc_samples = 200; 
slope_Hz_per_sec = 5e12;
n_chirps = 128;
isreal = 0;
elseif Config == 'E'
n_tx = 3; %1,2,3 
n_rx = 4; 
n_adc_samples = 200; 
sampling_rate_Sps = 10e6;
slope_Hz_per_sec = 5e12;
n_chirps = 128;
isreal = 0; 
elseif Config == 'F'
n_tx = 1; 
n_rx = 4; 
#n_adc_samples = 896; 
n_adc_samples = 256; 
#sampling_rate_Sps = 30e6;
sampling_rate_Sps = 5000;
slope_Hz_per_sec = 67e12;
n_chirps = 128;
isreal = 1; 
elseif Config == 'G'
n_tx = 3; %1,2,3,
n_rx = 4; 
n_adc_samples = 896; 
sampling_rate_Sps = 30e6;
slope_Hz_per_sec = 67e12;
n_chirps = 128 ; 
isreal = 1; 
end

Config
fid = fopen(fname,'r');

if isreal
dat = fread(fid,n_adc_samples*n_chirps*n_rx*n_tx,'uint16');
dat = dat - 2^15; % needs to be checked if 12 or 14bit data is used

dat = reshape(dat,8,[]); 
dat = dat([1,3,5,7,2,4,6,8],:);
if n_adc_bits ~= 16
l_max = 2^(n_adc_bits-1)-1;
dat(dat > l_max) = dat(dat > l_max) - 2^n_adc_bits;
end

lendata = max(size(dat))
dat = dat*norm_factor;
radar_data_all = reshape(dat, n_adc_samples, n_rx, n_tx, n_chirps,1);%Samples_Per_Chirp*2 for I and Q
data_real = radar_data_all;
cdat = data_real;
else
dat = fread(fid,n_adc_samples*n_chirps*n_rx*n_tx*2,'uint16');
dat = dat - 2^15;

if n_adc_bits ~= 16
l_max = 2^(n_adc_bits-1)-1;
dat(dat > l_max) = dat(dat > l_max) - 2^n_adc_bits;
end

dat = reshape(dat,8,[]); 
dat = dat([1,3,5,7,2,4,6,8],:);
cdat = dat(1:4,:) + 1i*dat(5:8,:);

cdat = cdat*norm_factor;

cdat = reshape(cdat, n_adc_samples,n_rx, n_tx, n_chirps,1); %Samples_Per_Chirp*2 for I and Q
cdat
end

fclose(fid);
pre_adc_dat = (cdat);

csvwrite('PostProcessADC.csv',dat)
size(dat)

size(pre_adc_dat)


x_axis = ((((0:n_adc_samples-1)/n_adc_samples)*sampling_rate_Sps)/slope_Hz_per_sec)*lightSpeed_meters_per_sec/2;

hann_win = hanning(size(pre_adc_dat(:,1,1,1),1));

% Normalize for window, and for 2D FFT gain.
hann_win = hann_win/sum(hann_win)/n_chirps;

for ik = 1:n_chirps
for rx_indx = 1:n_rx
for tx_indx = 1:n_tx
adc_dat(:,rx_indx,tx_indx,ik) = hann_win.*(pre_adc_dat(:,rx_indx,tx_indx,ik));
end
end
end


%% Plots. 

%% 1. 2D-FFT plot consisting of only the zero-velocity bin, and the noise floor. 
% The noise floor is an average of multiple high velocity bins.
% The data from all tx-rx combinations are seperately plotted.

figure(123);
hold off;
marker = '';
color = 'br';

for rx_indx = 1:n_rx
for tx_indx = 1:n_tx
subplot(n_rx,n_tx,tx_indx + (rx_indx-1)*n_tx);
fft2_out{rx_indx} = abs(fft2(squeeze(adc_dat(:,rx_indx, tx_indx,:))));
if rx_indx == 1 && tx_indx == 1
fft2_out_all = (fft2_out{rx_indx}.*fft2_out{rx_indx}); 
else
fft2_out_all = fft2_out_all + (fft2_out{rx_indx}.*fft2_out{rx_indx}); 
end

plot(x_axis, 20*log10(fft2_out{rx_indx}(:,1)),[color(1) '-' marker]); hold on;
plot(x_axis, 20*log10(fft2_out{rx_indx}(:,64-32:64+32)),[color(2) '-' marker]);
xlabel('Range (meters)');
ylabel('FFT Output (dBFs)');
if rx_indx == n_rx && tx_indx == n_tx
legend (['0-doppler bins' ], ['Noise floor']);
end
title(['Tx : ' num2str(tx_indx) ',' 'Rx : ' num2str(rx_indx) '.'])
grid minor; %view([0 0])
end
end

%% 2. 2D-FFT plot mesh-plot. 
% The data from all tx-rx combinations are non-coherently integrated.

figure(124);
fft2_out_all = fft2_out_all/n_rx;
imagesc(1:128,x_axis,10*log10(fftshift(fft2_out_all,2)));
title(['2D FFT (non-coherently integrated).']);
figure(125);

%% 3. 1D-FFT plot averaged across all chirps in a frame. 
% The data from all tx-rx combinations are seperately plotted.

for rx_indx = 1:n_rx
for tx_indx = 1:n_tx
subplot(n_rx,n_tx,tx_indx + (rx_indx-1)*n_tx);

fft_out_tmp = abs(fft(squeeze(adc_dat(:,rx_indx,tx_indx,:)),[],1)); 
fft_out_tmp = mean(fft_out_tmp.*fft_out_tmp,2);
fft2_out{rx_indx} = 10*log10(fft_out_tmp);
plot(x_axis, fft2_out{rx_indx},[color(1) '-' marker]); hold on; 
xlabel('Range (meters)');
ylabel('FFT Output (dBFs)');
title(['Tx : ' num2str(tx_indx) ',' 'Rx : ' num2str(rx_indx) '.'])
end
end

figure(126);
%% 4. ADC data plot. 
% data from all chirps are plotted on top of each other. 
% data from all tx-rx combinations are seperately plotted.

for rx_indx = 1:n_rx
for tx_indx = 1:n_tx
subplot(n_rx,n_tx,tx_indx + (rx_indx-1)*n_tx);

x_axis_samples = 1:n_adc_samples;
plot(x_axis_samples, real((squeeze(pre_adc_dat(:,rx_indx,tx_indx,1)/norm_factor))),['r-' marker]); hold on;
if isreal == 0
plot(x_axis_samples, imag((squeeze(pre_adc_dat(:,rx_indx,tx_indx,1)/norm_factor))),['b-' marker]); hold on;
end
xlabel('sample number');
ylabel('ADC codewords');
title(['Tx : ' num2str(tx_indx) ',' 'Rx : ' num2str(rx_indx) '.']);
grid minor; 
if isreal == 0 && rx_indx == n_rx && tx_indx == n_tx
legend (['Real ' ], ['Imag ']);
end
end
end

Thank you,