Modeling (almost) periodic moving average processes using cyclic statistics

Amod Dandawaté, Georgios B. Giannakis

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Estimating parameters of almost cyclostationary non-Gaussian moving average (MA) processes using noisy output-only data is considered. It is shown that second-order cyclic correlations of the output are generally insufficient in uniquely characterizing almost periodically time-varying MA(q) models, while third-order and higher order cumulants can be used to estimate their model parameters within a scale factor. Both linear and nonlinear identification algorithms for fixed and time-varying order q(t) are presented. Statistical model order determination procedures are also derived. Implementation issues are discussed and resistance to noise is claimed when the signal of interest has cycles distinct from the additive noise. Simulations are performed to verify the theoretical results.

Original languageEnglish (US)
Pages (from-to)673-684
Number of pages12
JournalIEEE Transactions on Signal Processing
Volume44
Issue number3
DOIs
StatePublished - 1996

Bibliographical note

Funding Information:
research was supported by ONR Grant N00014-93-1-0485. Parts of the results were presented at the Proceedings of the International. Conference. on Acoustlcs, Speech, and Signal Processing, (ICASSP’92), San Francisco, CA, USA, March 1992. The associate editor coordinating the review of this paper and approving it for publication was Dr. R. D. Preuss.

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