1
$\begingroup$

I am trying to estimate both azimuth and elevation angle, for which I simulated a URA, whose code is given below.

SNR_dB_vec = -20:5:10; % SNR levels Max_a = 180; Max_e = 90; grid_res = 5; sVar = 1; p = 100; % Number of time snapshots fs = 10^7; % Sampling frequency fc = 10^6; % Center frequency of narrowband sources Mx = 12; % Number of array elements on x axis My = 8; % Number of array elements on y axis cSpeed = 3*10^8 ; % Speed of light dist = 180; % Sensors (i.e., antennas) spacing in meters in both x and %y axes SOURCE_K = 4; % no.of sources*2 (for source 1 col1 and col3 are for %azimuth and for source 2 col2 and col4 are elevation) % The training sets azimuth_grid = -Max_a:grid_res:Max_a; elevation_grid = -Max_e:grid_res:Max_e; [Az, El] = meshgrid(azimuth_grid, elevation_grid); % Number of training samples num_samples = 2500; % Initialize doa matrix to store the results doa = zeros(num_samples, 4); for i = 1:num_samples % Randomly pick values for each column while true az1 = azimuth_grid(randi(numel(azimuth_grid))); el1 = elevation_grid(randi(numel(elevation_grid))); az2 = azimuth_grid(randi(numel(azimuth_grid))); el2 = elevation_grid(randi(numel(elevation_grid))); % Check if all values in the row are unique if numel(unique([az1, el1, az2, el2])) == 4 doa(i, :) = [az1, el1, az2, el2]; break; end end end G = size(doa,1); S = length(SNR_dB_vec); % number of the SNR levels N = 64; %total number of antenna elements R_ECM = zeros(Mx, Mx,2,G,S); % Expected covariance matrix (ECM) R_SCM = zeros(Mx, Mx,2,G,S); % Sampled covariance matrix (SCM) for i=1:S SNR_dB = SNR_dB_vec(i); noise_power = 10^(-SNR_dB/10); r_the = zeros(Mx, Mx,2,G); % Temporary expected covariance variable at a SNR level r_sam = zeros(Mx, Mx ,2,G); % Temporary Sampled covariance variable at a SNR level for ii=1:G SOURCE_angles = doa(ii,:); Ax = zeros(Mx, SOURCE_K); Ay = zeros(My - 1, SOURCE_K); for k = 1:2:SOURCE_K Ax(:, k) = exp(-1i*2*pi*fc*dist*(1/cSpeed)*(0:Mx-1)' ... *cosd(SOURCE_angles(k))*sind(SOURCE_angles(k+1))); Ay(:, k) = exp(-1i*2*pi*fc*dist*(1/cSpeed)*(1:My-1)' ... *sind(SOURCE_angles(k))*sind(SOURCE_angles(k+1))); end A = [Ax; Ay]; % Steering matrix of all sensors % The Expected covariance matrix for a angle-pair Ry_the = A*diag(ones(SOURCE_K,1))*A' + noise_power*eye(Mx+My-1); RMx_the = Ry_the(1:Mx, 1:Mx); RMy_the = Ry_the(Mx+1:Mx+My-1, Mx+1:Mx+My-1); % Original sensor is omitted RMy_the = [Ry_the(Mx+1:Mx+My-1, 1), RMy_the]; RMy_the = [[Ry_the(1, 1), Ry_the(1, Mx+1:Mx+My-1)]; RMy_the]; % (My x My) % The Sampled covariance matrix for a angle-pair S = sqrt(sVar)*randn(SOURCE_K, p).*exp(1i*(2*pi*fc*repmat((1:p)/fs, SOURCE_K, 1))); X = A*S; noiseCoeff = 1; Eta = sqrt(noiseCoeff)*randn(Mx + My - 1, p); Y = X + Eta; Ry_sam = Y*Y'/p; RMx_sam = Ry_sam(1:Mx, 1:Mx); % covariance matrix of signals received by ULA on X-axis RMy_sam = Ry_sam(Mx+1:Mx+My-1, Mx+1:Mx+My-1); % Original sensor is omitted RMy_sam = [Ry_sam(Mx+1:Mx+My-1, 1), RMy_sam]; RMy_sam = [[Ry_sam(1, 1), Ry_sam(1, Mx+1:Mx+My-1)]; RMy_sam]; % (My x My) % Real and Imaginary part for the ECM r_the(:,:,1,ii) = real(RMx_the); %%% when I am only trying to estimate the azimuth angles r_the(:,:,2,ii) = imag(RMx_the); % Real and Imaginary part for the SCM r_sam(:,:,1,ii) = real(RMx_sam); %%% when I am only trying to estimate the azimuth angles r_sam(:,:,2,ii) = imag(RMx_sam); end disp(['Processing SNR level:', num2str(i)]); R_ECM(:,:,:,:,i) = r_the; R_SCM(:,:,:,:,i) = r_sam; end % The angles Ground Truth angles = doa(:,1:2:4); %%% when I am only trying to estimate the azimuth angles % Save the DATA h5create(filename_ECM,'/angles',size(angles)); h5write(filename_ECM, '/angles', angles); h5create(filename_ECM,'/ECM',size(R_ECM)); h5write(filename_ECM, '/ECM', R_ECM); h5disp(filename_ECM); h5create(filename_SCM,'/angles',size(angles)); h5write(filename_SCM, '/angles', angles); h5create(filename_SCM,'/SCM',size(R_SCM)); h5write(filename_SCM, '/SCM', R_SCM); h5disp(filename_SCM);

I am using the deep learning model from the paper "Robust DOA Estimation Using Deep ComplexValued Convolutional Networks with Sparse Prior". In the paper they have used a ULA for predicting only one angle.

Firstly I tried to use the same model, and instead of different source angles at different columns, I used the azimuth angle in col1 and elevation in col2 for a single source system.

Similarly if we have 2 sources then col1 and col2 corresponds to the azimuth and elevation for source 1 and col3 and col4 corresponds to the azimuth and elevation for col2.

But this method is not providing accurate estimation, so I was thinking of using 2 separate models, one for estimating azimuth and another for elevation.

But in that case, I am not sure if I can use the expected and sample covariance matrix as inputs, because the covariance matrix is formed by the combination of both azimuth and elevation.

So, can anyone suggest what input features can I use, if I want to train 2 separate models ?

Improve this question
$\endgroup$
17
  • $\begingroup$ Are you doing this out of an interest in finding angles in this manner? Or have you not explored other methods. I don't have access to the paper but using a CNN just to estimate the angle of arrival is unnecessary. $\endgroup$ Commented Nov 5, 2024 at 17:33
  • 1
    $\begingroup$ Estimating azimuth and elevation is akin to two-dimensional sampling, which would necessitate a block covariance matrix. I would work on getting other DOA estimates, like MUSIC, working for 2-D first, because then you can be sure your covariance matrix is properly formed. Also, agreeing with Envidia, Neural Net methods are only useful in very low SNR very low snapshot cases, and require accurate training. You'll notice the CNN methods eventually perform worse in more practical scenarios than the others. $\endgroup$ Commented Nov 5, 2024 at 18:49
  • $\begingroup$ @Envidia, yes I am doing this out of interest, as there has been quite some literature on how to estimate DOA using neural networks, but there's not enough papers for 2D DOA estimation. So, I was just interested to try that. If I have to use a neural network then what features might be suitable as an input? $\endgroup$ Commented Nov 6, 2024 at 9:19
  • $\begingroup$ @Baddioes I have used a 2D Music algorithm to check if the simulated covariance matrix is correct and using the MUSIC algorithm I am able to estimate the DOAs. But I am not sure where I am going wrong with the neural network $\endgroup$ Commented Nov 6, 2024 at 11:53
  • 2
    $\begingroup$ It's honestly hard to say where you might be going wrong with the neural net. There's so many possible points along the path of training, validation, and testing where it could go wrong. I would probably start with your idea of independently estimating the azimuth and elevation angles and see if that works with your model. If it doesn't, then you know something is wrong with your model. If it does work, then something is wrong with your extension of the model to two dimensions. $\endgroup$ Commented Nov 6, 2024 at 14:38

0

Reset to default

Start asking to get answers

Find the answer to your question by asking.

Ask question

Explore related questions

See similar questions with these tags.