Spectrum Estimation by Noise-Compensated Data Extrapolation
Summary :
High-resolution spectrum estimation techniques have been extensively studied in recent publications. Knowledge of the noise variance is vital for spectrum estimation from noise-corrupted observations. This paper presents the use of noise compensation and data extrapolation for spectrum estimation. We assume that the observed data sequence can be represented by a set of autoregressive parameters. A recently proposed iterative algorithm is then used for noise variance estimation while autoregressive parameters are used for data extrapolation. We also present analytical results to show the exponential decay characteristics of the extrapolated samples and the frequency domain smoothing effect of data extrapolation. Some statistical results are also derived. The proposed noise-compensated data extrapolation approach is applied to both the autoregressive and FFT-based spectrum estimation methods. Finally, simulation results show the superiority of the method in terms of bias reduction and resolution improvement for sinusoids buried in noise.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E88-A No.3 pp.702-711
- Publication Date
- 2005/03/01
- Publicized
- Online ISSN
- DOI
- 10.1093/ietfec/e88-a.3.702
- Type of Manuscript
- PAPER
- Category
- Digital Signal Processing
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Keyword
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Jonah GAMBA, Tetsuya SHIMAMURA, "Spectrum Estimation by Noise-Compensated Data Extrapolation" in IEICE TRANSACTIONS on Fundamentals,
vol. E88-A, no. 3, pp. 702-711, March 2005, doi: 10.1093/ietfec/e88-a.3.702.
Abstract: High-resolution spectrum estimation techniques have been extensively studied in recent publications. Knowledge of the noise variance is vital for spectrum estimation from noise-corrupted observations. This paper presents the use of noise compensation and data extrapolation for spectrum estimation. We assume that the observed data sequence can be represented by a set of autoregressive parameters. A recently proposed iterative algorithm is then used for noise variance estimation while autoregressive parameters are used for data extrapolation. We also present analytical results to show the exponential decay characteristics of the extrapolated samples and the frequency domain smoothing effect of data extrapolation. Some statistical results are also derived. The proposed noise-compensated data extrapolation approach is applied to both the autoregressive and FFT-based spectrum estimation methods. Finally, simulation results show the superiority of the method in terms of bias reduction and resolution improvement for sinusoids buried in noise.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e88-a.3.702/_p
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@ARTICLE{e88-a_3_702,
author={Jonah GAMBA, Tetsuya SHIMAMURA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Spectrum Estimation by Noise-Compensated Data Extrapolation},
year={2005},
volume={E88-A},
number={3},
pages={702-711},
abstract={High-resolution spectrum estimation techniques have been extensively studied in recent publications. Knowledge of the noise variance is vital for spectrum estimation from noise-corrupted observations. This paper presents the use of noise compensation and data extrapolation for spectrum estimation. We assume that the observed data sequence can be represented by a set of autoregressive parameters. A recently proposed iterative algorithm is then used for noise variance estimation while autoregressive parameters are used for data extrapolation. We also present analytical results to show the exponential decay characteristics of the extrapolated samples and the frequency domain smoothing effect of data extrapolation. Some statistical results are also derived. The proposed noise-compensated data extrapolation approach is applied to both the autoregressive and FFT-based spectrum estimation methods. Finally, simulation results show the superiority of the method in terms of bias reduction and resolution improvement for sinusoids buried in noise.},
keywords={},
doi={10.1093/ietfec/e88-a.3.702},
ISSN={},
month={March},}
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TY - JOUR
TI - Spectrum Estimation by Noise-Compensated Data Extrapolation
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 702
EP - 711
AU - Jonah GAMBA
AU - Tetsuya SHIMAMURA
PY - 2005
DO - 10.1093/ietfec/e88-a.3.702
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E88-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2005
AB - High-resolution spectrum estimation techniques have been extensively studied in recent publications. Knowledge of the noise variance is vital for spectrum estimation from noise-corrupted observations. This paper presents the use of noise compensation and data extrapolation for spectrum estimation. We assume that the observed data sequence can be represented by a set of autoregressive parameters. A recently proposed iterative algorithm is then used for noise variance estimation while autoregressive parameters are used for data extrapolation. We also present analytical results to show the exponential decay characteristics of the extrapolated samples and the frequency domain smoothing effect of data extrapolation. Some statistical results are also derived. The proposed noise-compensated data extrapolation approach is applied to both the autoregressive and FFT-based spectrum estimation methods. Finally, simulation results show the superiority of the method in terms of bias reduction and resolution improvement for sinusoids buried in noise.
ER -
English
