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We have two classes of spectral estimators in LTPDA: one class based on the standard WOSA (Welch's Overlapped Segmented Average) and a modified version of WOSA which estimates the spectral quantities at frequencies spaced logarithmically.
To estimate the Power Spectral Density of a time-series, you can use the method ao/psd or the logarithmically spaced frequencies version, ao/lpsd. For example:
% Generate a time-series of random numbers
a = ao.randn(100, 10);
% Compute a PSD with the default configuration.
axx = psd(a);
% Plot the result
iplot(axx)
help ao/psd
>> keys('ao', 'psd')
------------------------------------------------
Default
------------------------------------------------
NFFT, WIN, PSLL, OLAP, ORDER, NAVS, TIMES, SCALE
% Generate a time-series of random numbers
a = ao.randn(100, 10);
% Create a plist to configure ao/psd
psdPlist = plist('navs', 10, 'scale', 'asd');
% Compute the PSD of the time-series with the given configuration plist
axx = psd(a, psdPlist);
% Plot the result
iplot(axx)
% Generate a time-series of random numbers
a = ao.randn(100, 10);
% Create a plist to configure ao/psd
psdPlist = plist('navs', 10, 'scale', 'asd');
% Compute the logarithmically spaced PSD of the time-series with the given configuration plist
axx = lpsd(a, psdPlist);
% Plot the result
iplot(axx)Transfer functions can be estimated from input and output data using the ao/tfe method (or the corresponding log-spaced method, ao/ltfe). Here's an example:
% Sample frequency of our data
fs = 10;
% Generate a time-series of random numbers
input = ao.randn(100, fs);
% Create a digital filter
myFilter = miir(plist('type', 'bandpass', 'fc', [0.5 1], 'fs', fs, 'order', 3));
% Filter the noise data
output = filter(input, myFilter);
% Estimate the transfer function from input to output
T = tfe(input, output);
% Plot the result
iplot(T) |
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