I am analyzing a transient signal presumably consisting of superposed exponentials. Such a case is indicated for the Prony analysis, but my data aren't noiseless enough, so I have turned to the Kumaresan-Tufts (KT) algorithm.
After reading the original article (Estimating the Parameters of Exponentially Damped Sinusoids and Pole-Zero Modeling in Noise, 1982) and a bit of googling I made use of the Matlab package Complex Exponential Analysis and more or less things work
My concern is now an intuition on the process - or, better, on its input parameters (cause FFT-like thinking is of course out of question):
- What should I expect of increasing or decreasing of model order?
- How can I assess the number of modes decinig for signal reconstruction and how can I pick them from the output parameters (dampings, frequencies, complex amplitudes)?
- Is there any recommended signal treatment before the KT method is applied? Such as detrending the data for FFT.
- Does time-inverting of the signal providing any help? It would mean damped exponentials instead of growing.
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