LTPDA Toolbox™ | contents |
The LTPDA method ao/ltfe estimates the transfer function of time-series signals, included in the input aos following the LPSD algorithm [1]. Spectral density estimates are not evaluated at frequencies which are linear multiples of the minimum frequency resolution 1/T, where T is the window lenght, but on a logarithmic scale. The algorithm takes care of calculating the frequencies at which to evaluate the spectral estimate, aiming at minimizing the uncertainty in the estimate itself, and to recalculate a suitable window length for each frequency bin.
Data are windowed prior to the estimation of the spectrum, by multiplying it with a spectral window object, and can be detrended by polinomial of time in order to reduce the impact of the border discontinuities. Detrending is performed on each individual window. The user can choose the quantity being given in output among ASD (amplitude spectral density), PSD (power spectral density), AS (amplitude spectrum), and PS (power spectrum).
b = ltfe(a1,a2,pl)
a1 and a2 are the 2 aos containing the input time series to be evaluated, b is the output object and pl is an optional parameter list.
The parameter list pl includes the following parameters:
If the user doesn't specify the value of a given parameter, the default value is used. |
The function makes logaritmic frequencyscale transfer functions estimates between the 2 input aos, and the output will contain the transfer function estimate from the first ao to the second.
The algorithm is implemented according to [1]. The sample variance is computed according to:
is the coherence function. In the LPSD algorithm, the first frequencies bins are usually computed using a single segment containing all the data. For these bins, the standard deviation of the mean is set to Inf.
Evaluation of the transfer function between two time-series represented by: a low frequency sinewave signal superimposed to white noise, and a low frequency sinewave signal at the same frequency, phase shifted and with different amplitude, superimposed to white noise.
% Parameters nsecs = 1000; fs = 10; nfft = 1000; % Create input AOs x = ao(plist('waveform','sine wave','f',0.1,'A',1,'nsecs',nsecs,'fs',fs)) + ... ao(plist('waveform','noise','type','normal','nsecs',nsecs,'fs',fs)); x.setYunits('m'); y = ao(plist('waveform','sine wave','f',0.1,'A',2,'nsecs',nsecs,'fs',fs,'phi',90)) + ... 4*ao(plist('waveform','noise','type','normal','nsecs',nsecs,'fs',fs)); y.setYunits('V'); % Compute transfer function Txy = ltfe(x,y,plist('win',specwin('Kaiser',1,200),'nfft',nfft)); % Plot iplot(Txy);
Log-scale cross coherence density estimates | Fitting Algorithms |
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