Polyspectral analysis of (almost) cyclostationary signals: LPTV system identification and related applications
- 9 December 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 377-382 vol.1
- https://doi.org/10.1109/acssc.1991.186476
Abstract
Polyspectral estimators are proposed for (almost) cyclostationary signals and are shown to be consistent and asymptotically normal. These estimators are used for identification of linear (almost) periodically time-varying systems. Both nonparametric and parametric approaches are described for input-output and output only identification. Statistical analysis of nonstationary signals with missing observations is treated and tests are developed for checking the presence of cycle frequencies. Frequency estimation and detection of coupling are addressed in the cyclic domain without resorting to phase randomization. All methods are proven to be insensitive to stationary noise and use consistent single record estimators.Keywords
This publication has 12 references indexed in Scilit:
- GRAPHICAL METHODS FOR DETERMINING THE PRESENCE OF PERIODIC CORRELATIONJournal of Time Series Analysis, 1991
- Cumulant-based approach to harmonic retrieval and related problemsIEEE Transactions on Signal Processing, 1991
- Exploitation of spectral redundancy in cyclostationary signalsIEEE Signal Processing Magazine, 1991
- A study of second- and third-order spectral procedures and maximum likelihood in the identification of a bilinear systemIEEE Transactions on Acoustics, Speech, and Signal Processing, 1990
- Time and lag recursive computation of cumulants from a state-space modelIEEE Transactions on Automatic Control, 1990
- Nonparametric time series analysis for periodically correlated processesIEEE Transactions on Information Theory, 1989
- Bispectrum estimation: A parametric approachIEEE Transactions on Acoustics, Speech, and Signal Processing, 1985
- A single-record estimator for correlation functions of nonstationary random processesProceedings of the IEEE, 1981
- On Periodic and Multiple AutoregressionsThe Annals of Statistics, 1978
- Spectral representation of a periodic nonstationary random processIEEE Transactions on Information Theory, 1971