General smoothing techniques for estimating deterministic sinusoidal frequencies from noisy data

Samuel Itzikowitz, Amir Averbuch

Research output: Contribution to journalConference articlepeer-review

Abstract

General high-resolution smoothing techniques for estimating deterministic sinusoidal frequencies from short-record noisy data are presented. These techniques are general in the sense that methods such as the modified least squares Prony method, as well as those which are based on eigenvector decompositions, may be considered as special cases of them. The theoretical basis of these smoothing techniques is discussed, and their performance in the presence of white Gaussian noise at low signal-to-noise ratio (SNR) is examined. It is shown that close to the threshold of the maximum-likelihood method (SNR ≈3 dB) these symmetric smoothing techniques provide better accuracy than any other current method.

Original languageEnglish
Pages (from-to)2583-2586
Number of pages4
JournalProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
Volume5
StatePublished - 1990
Event1990 International Conference on Acoustics, Speech, and Signal Processing: Speech Processing 2, VLSI, Audio and Electroacoustics Part 2 (of 5) - Albuquerque, New Mexico, USA
Duration: 3 Apr 19906 Apr 1990

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