Harmonics in Multiplicative and Additive Noise: Performance Analysis of Cyclic Estimators

Guotong Zhou, Georgios B. Giannakis

Research output: Contribution to journalArticlepeer-review

68 Scopus citations

Abstract

Multiplicative noise causes smearing of spectral lines and thus hampers frequency estimation relying on conventional spectral analysis. In contrast, cyclic mean and correlation statistics have proved to be useful for harmonic retrieval in the presence of multiplicative and additive noise of arbitrary color and distribution. Performance analysis of cyclic estimators is carried through both for nonzero and zero mean multiplicative noises. Cyclic estimators are shown to be asymptotically equivalent to certain nonlinear least squares estimators, and are also compared with the maximum likelihood ones. Large sample variance expressions of the cyclic estimators are derived and compared with the corresponding Cramér-Rao bounds when the noises are white Gaussian. It is demonstrated that previously well established results on constant amplitude harmonics are special cases of our analysis. Simulations not only validate the large sample performance analysis, but also provide concrete examples regarding relative statistical efficiency of the cyclic estimators.

Original languageEnglish (US)
Pages (from-to)1445-1460
Number of pages16
JournalIEEE Transactions on Signal Processing
Volume43
Issue number6
DOIs
StatePublished - Jun 1995

Fingerprint Dive into the research topics of 'Harmonics in Multiplicative and Additive Noise: Performance Analysis of Cyclic Estimators'. Together they form a unique fingerprint.

Cite this