LTPDA Toolbox™

IFO/Temperature Example - Spectral Analysis

Empirical Transfer Function estimation

Let's run this exercise on empirical estimation of Transfer Functions.

The idea of the exercise is the following:

simulate some white noise x(t)
build a band-pass filter F
pass the input noise x(t) through the filter and add some more noise yn(t) at the output so to have y = F*x(t) + yn(t)
evaluate and plot the transfer function x -> y

In a flow diagram, the representation is as follows:

The command-line sequence is the following:

%% General definitions
nsecs = 10000;
fs = 1;
%% Input noise
x = ao(plist('waveform', 'noise', 'sigma', 3, 'fs', fs, 'nsecs', nsecs, 'yunits', 'V'))

%% Filter 
bp_filter = miir(plist('type', 'bandpass', 'fc', [0.01 0.1], 'fs', 1, 'order', 3, 'iunits', 'V', 'ounits', 'A'))
xf = simplifyYunits(filter(x, bp_filter))

%% Output noise
yn = ao(plist('waveform', 'noise', 'sigma', 1, 'fs', fs, 'nsecs', xf.nsecs, 'yunits', 'A'))
y = xf + yn

%% Plotting input and output noise
xx = psd(x, plist('scale', ...
                  'ASD', ...
                  'nfft', 1000))
yy = psd(y, plist('scale', 'ASD', ...
                  'nfft', 1000))
iplot(xx, yy, plist('Arrangement', 'subplots', 'YRanges', {[1e-1 1e1], [1e-2 1e2]}));

Now we can proceed with the call to the ao/tfe method. The parameter list is very similar to the one employed for the other spectral estimators:

Key	Value	Description
`NFFT`	1000	The number of samples defining the length of the window to apply
`WIN`	'BH92'	Or a different one, if you want.
`OLAP`	-1	Overlap will be chosen based on the window properties
`ORDER`	0	Segment-wise detrending up to order 0

The command line is the following:

%% Estimate the x->y transfer function
tfxy = tfe(x, y, plist('nfft', 1000, 'win', 'BH92', 'olap', -1, 'order', 0));

We also would like to evaluate the expected transfer function x->y, which is obviously the filter transfer function, or response. This can be calculated by means of the

miir/resp

method. A detailed description of digital filtering is available in the User Manual dedicated section and will be touched upon in this topic; here let's just use the simplest form, where the needed parameter is a list of the frequency to evaluate the response at:

Key	Value	Description
`F`	tfxy.x	a vector of frequency values or an `ao` whereby the x-axis is taken for the frequency values

So we can just pass the x field of the fsdata ao containing the transfer function estimate. However, we can also just pass the AO itself. In which case, the resp function will take the X values from the AO.

The command line is the following:

%% Evaluate the expected x->y transfer function
rf = resp(bp_filter, plist('f', tfxy))

Eventually let's look at the results:

%% Plotting estimated and expected transfer functions
iplot(tfxy, rf, plist('colors', {[1 0 0], [0 0 0]}, 'YRanges', {[1e-2 1e2], [-200 200], [1e-2 1e2], [-200 200]}))

Example 3: Log-scale PSD on MDC1 data

IFO/Temperature Example - Spectral Analysis