LTPDA Toolbox™ | contents |
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)
Saving and loading objects | Preparing data segments (splitting) |
©LTP Team