Accurate estimates of the autocorrelation or power spectrum can be obtained with a parametric model (AR, MA or ARMA). With automatic inference, not only the model parameters but also the model structure are determined from the data. It is assumed that the ARMASA toolbox is present. This toolbox can be downloaded from the MATLAB Central file exchange at www.mathworks.com
The applications of this toolbox are:
- Reduced statistics ARMAsel: A compact yet accurate ARMA model is obtained based on a given power spectrum. Can be used for generation of colored noise with a prescribed spectrum.
- ARfil algorithm: The analysis of missing data/irregularly sampled signals
- Subband analysis: Accurate analysis of a part of the power spectrum
- Vector Autoregressive modeling: The automatic analysis of auto- and crosscorrelations and spectra
- Detection: Generally applicable test statistic to determine whether two signals have been generated by the same process or not. Based on the Kullback-Leibler index or Likelihood Ratio.
- Analysis of segments of data, possibly of unequal length.
For background information see my PhD thesis, available at http://www.dcsc.tudelft.nl/Research/PubSSC/thesis_sdewaele.html
Stijn de Waele (2023). Automatic Spectral Analysis (https://www.mathworks.com/matlabcentral/fileexchange/3680-automatic-spectral-analysis), MATLAB Central File Exchange. 检索来源 .
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* included _e m-files for reduced statistics ARMAsel;