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Hello,

I have a small question on FFT based adaptive beamforming based on Spectral Matrix Inversion technique.

I have estimated the Spectral Matrix (Sxx) from my data by dividing it into blocks and averaging them for each bin, to get sufficient rank on the matrix.

Now my question is :

To produce the MVDR weighting vector (8x1 vector in my case since I have 8 microphones) for each bin, I multiply the inverse of Sxx for each bin (say bin#1) with the steering vector 'a' given by exp(i*2*pi*D*omega), where D is the set of time delays(8x1 vector) pointing towards look direction(say +10°). Here what should be 'omega'? Isit equal to the first frequency bin of data from first channel and so on for all the bins? please clarify.

Honglei Chen
on 24 Sep 2012

Looks like you are doing subband MVDR. It should be the frequency for each corresponding band.

Honglei Chen
on 26 Sep 2012

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zohar
on 9 Oct 2012

Hi zozo,

You allready posted a question

http://www.mathworks.com/matlabcentral/answers/39458-covariance-matrix-stimation-in-mvdr-beamforming

Combining the two questions ,evrything looks OK!

1) What is the shape of the array ? In the previous question you mentiond that it's spherical shape.

2) It's seems that in step 9 you want to go back to time domain, I do not understand what you doing ! can you supply the relevant matlab code ?

3) Insted of steps 7-9 calc P - the narrowband power spectrum, where P is:

P = zeros(Nbins,Nbeam)

for k = 1:Nbins

% for each bin

% calc A - sterring matrix.

% calc IRf - Inverse spatial covariance matrix.

P(k,:) = real(Nbeam./sum(A'*IRf*A));

end

Now calc

Plog = 20*log10(P);

mesh(Plog);

And let me know what the result is...

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