On nonparametric spectral estimation
Tomas Sundin,Petre Stoica +1 more
- Vol. 18, Iss: 2, pp 169-181
TLDR
The Cramér-Rao bound for a general nonparametric spectral estimation problem is derived under a local smoothness condition and under the aforementioned condition the Thomson method (TM) and Daniell method (DM) for power spectral density (PSD) estimation can be interpreted as approximations of the maximum likelihood PSD estimator.Abstract:
In this paper the Cramer-Rao bound (CRB) for a general nonparametric spectral estimation problem is derived under a local smoothness condition (more exactly, the spectrum is assumed to be well approximated by a piecewise constant function). Furthermore it is shown that under the aforementioned condition the Thomson (TM) and Danieli (DM) methods for power spectral density (PSD) estimation can be interpreted as approximations of the maximum likelihood PSD estimator. Finally the statistical efficiency of the TM and DM as nonparametric PSD estimators is examined and also compared to the CRB for ARMA-based PSD estimation. In particular for broadband signals, the TM and DM almost achieve the derived nonparametric performance bound and can therefore be considered to be nearly optimal.read more
Citations
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Cognitive radio: brain-empowered wireless communications
TL;DR: Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks: radio-scene analysis, channel-state estimation and predictive modeling, and the emergent behavior of cognitive radio.
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Signal Processing in Cognitive Radio
TL;DR: The fundamental signal-processing aspects involved in developing a fully functional cognitive radio network, including spectrum sensing and spectrum sculpting are described.
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Filter Bank Spectrum Sensing for Cognitive Radios
TL;DR: This paper proposes filter banks as a tool for spectrum sensing in CR systems and proposes a spectrum analyzer that is contrasted with the Thomson's multitaper (MT) method - a method that in the recent literature has been recognized as the best choice for Spectrum sensing inCR systems.
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Spectral estimation on a sphere in geophysics and cosmology
F. A. Dahlen,Frederik J. Simons +1 more
TL;DR: In this paper, the authors address the problem of estimating the spherical-harmonic power spectrum of a statistically isotropic scalar signal s(r) from noise-contaminated data d(r + n(r)) on a region R of the unit sphere.
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Jackknifing Multitaper Spectrum Estimates
TL;DR: Examples of jackknifing multitaper estimates of spectra, coherences, and frequency estimates include barometric pressure data and a reexamination of the 663-year record of Nile River levels, a process claimed to be long-memory.
References
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Journal ArticleDOI
Spectrum estimation and harmonic analysis
TL;DR: In this article, a local eigenexpansion is proposed to estimate the spectrum of a stationary time series from a finite sample of the process, which is equivalent to using the weishted average of a series of direct-spectrum estimates based on orthogonal data windows to treat both bias and smoothing problems.
Book
Digital spectral analysis : with applications
TL;DR: This new book provides a broad perspective of spectral estimation techniques and their implementation concerned with spectral estimation of discretespace sequences derived by sampling continuousspace signals.
Book
Introduction to spectral analysis
Petre Stoica,Randolph L. Moses +1 more
TL;DR: This chapter presents a meta-analyses of the nonparametric methods used in the construction of the Cramer-Rao Bound Tools, which were developed in the second half of the 1990s to address the problem of boundedness in the discrete-time model.
Digital spectral analysis with applications
TL;DR: In this article, a broad perspective of spectral estimation techniques and their implementation is provided, focusing on spectral estimation of discretespace sequences derived by sampling continuous space signals, including parametric methods, minimum variance method, eigenanalysis-based estimators, multichannel methods, and twodimensional methods.