04-02-2008 06:03 AM
What does noise subspace means in the TSA Stochastic
State-Space Modeling. If I use as a noise subspace 0.1% means that I almost
assume that in my recorded signal there is no noise? What happen if I put 99%?
I am recording signal with considerable quantity of noise but I don't know
how to use this parameter in order to get good results. At the beginning I am getting good results with very low factors (liken 0.1%)
04-10-2008 03:04 AM
Richard Keromen
04-10-2008 04:47 AM
04-10-2008
05:15 AM
- last edited on
09-16-2025
04:06 PM
by
Content Cleaner
Hi
Here is a doc where you'll find all you need :
What is noise subspace ?
"The Noise Subspace value is set to 90% to compensate for additive noise
in the measurements. For a fixed number of resonance components, a large
dimension for the Noise Subspace parameter results in a large dimension
for the signal subspace you use to describe a time series. A large dimension
for the signal subspace helps you reduce the modeling error for the time
series. However, an excessively large dimension may introduce spurious
resonance components if the time series does not contain much noise. In
Figure 5-4, the Modes array accurately indicates the attribute of the
resonance components that the synthesized time series contains."
What is model order ?
"The model order determines the number of modes that a modal parametric
model contains. A model can contain real modes or pairs of complex
conjugate modes. A real mode generates a resonance component at 0 Hz or
at the Nyquist frequency. A pair of conjugate complex modes generates a
resonance component with a positive frequency and discards the conjugate
resonance component with the corresponding negative frequency. To
search for m positive resonance components, you must specify the model
order to be at least 2 × m. If a time series contains a large offset, you need
to set the model order to at least 2 × m + 1 to allow for a real mode.
Structural vibration time series typically do not have offsets, so you can use
a model order of 2 × m."
Regards
Richard Keromen