On the accuracy of frequency determination by an autoregressive spectral estimator
Abstract The accuracy of frequency determination by a least squares technique for an autoregressive spectral estimator is studied and compared with the Fourier method. Using numerical tests the probability distribution function of the peak location is calculated. The autoregressive filter order is o...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kovács, G. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
1983 |
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| Schlagwörter: |
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| Anmerkung: |
© D. Reidel Publishing Co 1983 |
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| Übergeordnetes Werk: |
Enthalten in: Solar physics - Kluwer Academic Publishers, 1967, 82(1983), 1-2 vom: Jan., Seite 123-128 |
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| Übergeordnetes Werk: |
volume:82 ; year:1983 ; number:1-2 ; month:01 ; pages:123-128 |
| Links: |
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| DOI / URN: |
10.1007/BF00145552 |
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OLC2033552624 |
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| 520 | |a Abstract The accuracy of frequency determination by a least squares technique for an autoregressive spectral estimator is studied and compared with the Fourier method. Using numerical tests the probability distribution function of the peak location is calculated. The autoregressive filter order is optimized in the sense of minimum variance of the peak location. Simple sinusoidal signals with additive Gaussian noise are considered and the effect of other components is only indicated. Generally, a filter order between 1/3 and 1/2 of the total data number and a not very dense data sampling, gives the most stable spectrum. The results are numerical. | ||
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10.1007/BF00145552 doi (DE-627)OLC2033552624 (DE-He213)BF00145552-p DE-627 ger DE-627 rakwb eng 530 VZ 16,12 ssgn Kovács, G. verfasserin aut On the accuracy of frequency determination by an autoregressive spectral estimator 1983 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © D. Reidel Publishing Co 1983 Abstract The accuracy of frequency determination by a least squares technique for an autoregressive spectral estimator is studied and compared with the Fourier method. Using numerical tests the probability distribution function of the peak location is calculated. The autoregressive filter order is optimized in the sense of minimum variance of the peak location. Simple sinusoidal signals with additive Gaussian noise are considered and the effect of other components is only indicated. Generally, a filter order between 1/3 and 1/2 of the total data number and a not very dense data sampling, gives the most stable spectrum. The results are numerical. Fourier Distribution Function Probability Distribution Gaussian Noise Probability Distribution Function Enthalten in Solar physics Kluwer Academic Publishers, 1967 82(1983), 1-2 vom: Jan., Seite 123-128 (DE-627)129856010 (DE-600)281593-X (DE-576)015160033 0038-0938 nnns volume:82 year:1983 number:1-2 month:01 pages:123-128 https://doi.org/10.1007/BF00145552 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-AST SSG-OPC-AST GBV_ILN_11 GBV_ILN_22 GBV_ILN_40 GBV_ILN_47 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2279 GBV_ILN_2286 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4305 GBV_ILN_4306 AR 82 1983 1-2 01 123-128 |
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10.1007/BF00145552 doi (DE-627)OLC2033552624 (DE-He213)BF00145552-p DE-627 ger DE-627 rakwb eng 530 VZ 16,12 ssgn Kovács, G. verfasserin aut On the accuracy of frequency determination by an autoregressive spectral estimator 1983 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © D. Reidel Publishing Co 1983 Abstract The accuracy of frequency determination by a least squares technique for an autoregressive spectral estimator is studied and compared with the Fourier method. Using numerical tests the probability distribution function of the peak location is calculated. The autoregressive filter order is optimized in the sense of minimum variance of the peak location. Simple sinusoidal signals with additive Gaussian noise are considered and the effect of other components is only indicated. Generally, a filter order between 1/3 and 1/2 of the total data number and a not very dense data sampling, gives the most stable spectrum. The results are numerical. Fourier Distribution Function Probability Distribution Gaussian Noise Probability Distribution Function Enthalten in Solar physics Kluwer Academic Publishers, 1967 82(1983), 1-2 vom: Jan., Seite 123-128 (DE-627)129856010 (DE-600)281593-X (DE-576)015160033 0038-0938 nnns volume:82 year:1983 number:1-2 month:01 pages:123-128 https://doi.org/10.1007/BF00145552 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-AST SSG-OPC-AST GBV_ILN_11 GBV_ILN_22 GBV_ILN_40 GBV_ILN_47 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2279 GBV_ILN_2286 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4305 GBV_ILN_4306 AR 82 1983 1-2 01 123-128 |
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10.1007/BF00145552 doi (DE-627)OLC2033552624 (DE-He213)BF00145552-p DE-627 ger DE-627 rakwb eng 530 VZ 16,12 ssgn Kovács, G. verfasserin aut On the accuracy of frequency determination by an autoregressive spectral estimator 1983 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © D. Reidel Publishing Co 1983 Abstract The accuracy of frequency determination by a least squares technique for an autoregressive spectral estimator is studied and compared with the Fourier method. Using numerical tests the probability distribution function of the peak location is calculated. The autoregressive filter order is optimized in the sense of minimum variance of the peak location. Simple sinusoidal signals with additive Gaussian noise are considered and the effect of other components is only indicated. Generally, a filter order between 1/3 and 1/2 of the total data number and a not very dense data sampling, gives the most stable spectrum. The results are numerical. Fourier Distribution Function Probability Distribution Gaussian Noise Probability Distribution Function Enthalten in Solar physics Kluwer Academic Publishers, 1967 82(1983), 1-2 vom: Jan., Seite 123-128 (DE-627)129856010 (DE-600)281593-X (DE-576)015160033 0038-0938 nnns volume:82 year:1983 number:1-2 month:01 pages:123-128 https://doi.org/10.1007/BF00145552 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-AST SSG-OPC-AST GBV_ILN_11 GBV_ILN_22 GBV_ILN_40 GBV_ILN_47 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2279 GBV_ILN_2286 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4305 GBV_ILN_4306 AR 82 1983 1-2 01 123-128 |
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10.1007/BF00145552 doi (DE-627)OLC2033552624 (DE-He213)BF00145552-p DE-627 ger DE-627 rakwb eng 530 VZ 16,12 ssgn Kovács, G. verfasserin aut On the accuracy of frequency determination by an autoregressive spectral estimator 1983 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © D. Reidel Publishing Co 1983 Abstract The accuracy of frequency determination by a least squares technique for an autoregressive spectral estimator is studied and compared with the Fourier method. Using numerical tests the probability distribution function of the peak location is calculated. The autoregressive filter order is optimized in the sense of minimum variance of the peak location. Simple sinusoidal signals with additive Gaussian noise are considered and the effect of other components is only indicated. Generally, a filter order between 1/3 and 1/2 of the total data number and a not very dense data sampling, gives the most stable spectrum. The results are numerical. Fourier Distribution Function Probability Distribution Gaussian Noise Probability Distribution Function Enthalten in Solar physics Kluwer Academic Publishers, 1967 82(1983), 1-2 vom: Jan., Seite 123-128 (DE-627)129856010 (DE-600)281593-X (DE-576)015160033 0038-0938 nnns volume:82 year:1983 number:1-2 month:01 pages:123-128 https://doi.org/10.1007/BF00145552 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-AST SSG-OPC-AST GBV_ILN_11 GBV_ILN_22 GBV_ILN_40 GBV_ILN_47 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2279 GBV_ILN_2286 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4305 GBV_ILN_4306 AR 82 1983 1-2 01 123-128 |
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Abstract The accuracy of frequency determination by a least squares technique for an autoregressive spectral estimator is studied and compared with the Fourier method. Using numerical tests the probability distribution function of the peak location is calculated. The autoregressive filter order is optimized in the sense of minimum variance of the peak location. Simple sinusoidal signals with additive Gaussian noise are considered and the effect of other components is only indicated. Generally, a filter order between 1/3 and 1/2 of the total data number and a not very dense data sampling, gives the most stable spectrum. The results are numerical. © D. Reidel Publishing Co 1983 |
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Abstract The accuracy of frequency determination by a least squares technique for an autoregressive spectral estimator is studied and compared with the Fourier method. Using numerical tests the probability distribution function of the peak location is calculated. The autoregressive filter order is optimized in the sense of minimum variance of the peak location. Simple sinusoidal signals with additive Gaussian noise are considered and the effect of other components is only indicated. Generally, a filter order between 1/3 and 1/2 of the total data number and a not very dense data sampling, gives the most stable spectrum. The results are numerical. © D. Reidel Publishing Co 1983 |
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Abstract The accuracy of frequency determination by a least squares technique for an autoregressive spectral estimator is studied and compared with the Fourier method. Using numerical tests the probability distribution function of the peak location is calculated. The autoregressive filter order is optimized in the sense of minimum variance of the peak location. Simple sinusoidal signals with additive Gaussian noise are considered and the effect of other components is only indicated. Generally, a filter order between 1/3 and 1/2 of the total data number and a not very dense data sampling, gives the most stable spectrum. The results are numerical. © D. Reidel Publishing Co 1983 |
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Reidel Publishing Co 1983</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The accuracy of frequency determination by a least squares technique for an autoregressive spectral estimator is studied and compared with the Fourier method. Using numerical tests the probability distribution function of the peak location is calculated. The autoregressive filter order is optimized in the sense of minimum variance of the peak location. Simple sinusoidal signals with additive Gaussian noise are considered and the effect of other components is only indicated. Generally, a filter order between 1/3 and 1/2 of the total data number and a not very dense data sampling, gives the most stable spectrum. 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