Amplitude estimation for sinusoidal signals with application to system identification

Petre Stoica, Hongbin Li, Jian Li

Research output: Contribution to journalArticlepeer-review

Abstract

This paper considers the problem of amplitude estimation of sinusoidal signals from observations corrupted by colored noise. A relatively large number of amplitude estimators are described that encompass least squares (LS) and weighted least squares (WLS) methods. Additionally, filter bank approaches, which are widely used for spectral analysis, are extended to amplitude estimation. Specifically, we consider the recently introduced matched-fllterbank (MAPI) approach and show that by appropriately designing the prefllters, the MAPI approach to amplitude estimation includes the WLS approach. The amplitude estimation techniques discussed in this paper do not model the observation noise, and yet, they are all asymptotically statistically efficient. It is, however, their different finite-sample properties that are of particular interest to this study. Numerical examples are provided to illustrate the differences among the various amplitude estimators. Although amplitude estimation applications are numerous, we focus herein only on system identification using sinusoidal probing signals.

Original languageEnglish
Pages (from-to)281-282
Number of pages2
JournalIEEE Transactions on Signal Processing
Volume47
Issue number1
StatePublished - 1999

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