Frequency-Selective Noise-Compensated Autoregressive Estimation

This paper presents a novel method for noise-compensated autoregressive estimation founded on the maximum-likelihood of the spectral samples. This framework yields a nonlinear optimization problem that can be revamped as a reweighted least-square problem. The resulting spectral weighting function tu...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems. I, Regular papers Regular papers, 2011-10, Vol.58 (10), p.2469-2476
Main Author: Weruaga, L.
Format: Article
Language:English
Subjects:
Approximation
Autoregressive analysis
Autoregressive processes
Circuits
Equations
Foundations
Least squares method
Mathematical model
Maximum likelihood estimation
maximum-likelihood
noise
Optimization
Signal to noise ratio
Spectra
spectral estimation
State of the art
Weighting functions
Wiener filter
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Summary:This paper presents a novel method for noise-compensated autoregressive estimation founded on the maximum-likelihood of the spectral samples. This framework yields a nonlinear optimization problem that can be revamped as a reweighted least-square problem. The resulting spectral weighting function turns out to be the square of the Wiener filter, this meaning that spectral regions with higher signal-to-noise ratio are more relevant in the estimation. Furthermore, this frequency-selective scenario allows us to interpret this problem as one of incomplete samples. From that perspective, an approximate accuracy bound for autoregressive analysis in noise is deduced. Simulated experiments prove the validity of the method foundations, showing as well the excellent performance of the numerical algorithm versus state-of-the-art techniques.
ISSN:1549-8328
1558-0806
DOI:10.1109/TCSI.2011.2142830