Name
Title | ||
|---|---|---|
General smoothing techniques for estimating deterministic sinusoidal frequencies from noisy data |
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 |
Year | DOI | Venue |
|---|---|---|
1990 | 10.1109/ICASSP.1990.116138 | Albuquerque, NM |
Keywords | DocType | ISSN |
parameter estimation,signal processing,spectral analysis,white noise,deterministic sinusoidal frequencies,eigenvector decompositions,frequency estimation,high-resolution smoothing techniques,low signal-to-noise ratio,maximum-likelihood method,modified least squares prony method,noisy data,white gaussian noise,signal to noise ratio,computer science,polynomials,maximum likelihood method,gaussian noise,numerical simulation,high resolution,noise measurement,eigenvectors,maximum likelihood estimation,least square | Conference | 1520-6149 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Itzikowitz, S. | 1 | 0 | 0.34 |
| Averbuch, A. | 2 | 0 | 0.34 |