Hi Gisela,
I understand that you are trying to estimate Power Spectral Density (PSD) and wants to compare the traditional PSD with Global Wavelet Spectrum (GWS) using ‘db6’ wavelet.
To estimate PSD, you can utilize the ‘pwelch’ function that follows Welch’s method.
Refer to the code snippet below for estimating PSD:
% Compute traditional PSD using Welch's method
[pxx, f] = pwelch(signal, [], [], [], fs);
Also, for the generation of GWS, you can utilize the ‘wavedec’ function with ‘db6’ wavelet.
Refer to the implementation below for better understanding:
% Perform DWT using db6
maxLevel = 16; % Maximum level of decomposition
[coeffs, levels] = wavedec(signal, maxLevel, 'db6');
% Calculate the corresponding frequencies for each scale
center_frequency = 0.666 * (fs / 2);
% Calculate frequencies for each scale
frequencies = center_frequency ./ (2.^(0:maxLevel-1));
% Estimate PSD from DWT coefficients (GWS)
gws = zeros(size(frequencies));
for i = 1:maxLevel
% Reconstruct signal from detail coefficients at each level
reconstructed = wrcoef('d', coeffs, levels, 'db6', i);
% Compute power of reconstructed signal
gws(i) = sum(reconstructed.^2);
end
After calculating the values of both PSD and GWS, you can compare them by simply plotting the values.
For more information, you can refer to the MATLAB Documentation of ‘pwelch’ and ‘wavedec’ functions.
I hope this helps!
