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Spectral density estimation

About: Spectral density estimation is a research topic. Over the lifetime, 5391 publications have been published within this topic receiving 123105 citations.


Papers
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Journal ArticleDOI
TL;DR: This paper addresses the reconstruction of compactly supported functions from non-uniform samples of their Fourier transform with convolutional gridding and uniform re-sampling, and investigates the reconstruction accuracy as it relates to sampling density.
Abstract: This paper addresses the reconstruction of compactly supported functions from non-uniform samples of their Fourier transform. We briefly investigate the consequences of acquiring non-uniform spectral data. We summarize two often applied reconstruction methods, convolutional gridding and uniform re-sampling, and investigate the reconstruction accuracy as it relates to sampling density. Finally, we provide preliminary results from employing spectral re-projection methods in the reconstruction.

32 citations

Patent
29 Apr 1993
TL;DR: In this article, a spectral estimation technique was used to estimate the angle-of-arrival (AOA) of radar pulses with respect to a ground or airborne based platform.
Abstract: The method provides the capability to estimate the angle-of-arrival (AOA) of radar pulses with respect to a ground or airborne based platform. It utilizes a spectral estimation technique which extracts the periodic properties of radar signals whose time-of-arrivals (TOAs) have been tagged by an Electronic Warfare receiver. The method can be used in a multiple signal environment and can separate and individually measure AOA of emitters that are spatially very close but have incommensurate Pulse Repetition Intervals (PRIs).

32 citations

Proceedings ArticleDOI
22 Jun 2014
TL;DR: A novel sparse Bayesian learning (SBL) algorithm is developed, which estimates the atom parameters along with the model order and weighting coefficients, which outperforms state-of-the-art subspace and compressed sensing methods.
Abstract: This paper concerns sparse decomposition of a noisy signal into atoms which are specified by unknown continuous-valued parameters. An example could be estimation of the model order, frequencies and amplitudes of a superposition of complex sinusoids. The common approach is to reduce the continuous parameter space to a fixed grid of points, thus restricting the solution space. In this work, we avoid discretization by working directly with the signal model containing parameterized atoms. Inspired by the “fast inference scheme” by Tipping and Faul we develop a novel sparse Bayesian learning (SBL) algorithm, which estimates the atom parameters along with the model order and weighting coefficients. Numerical experiments for spectral estimation with closely-spaced frequency components, show that the proposed SBL algorithm outperforms state-of-the-art subspace and compressed sensing methods.

32 citations

Journal ArticleDOI
01 Oct 1980
TL;DR: This letter clarifies some recent misleading comments made on spectral estimation by giving specific references and simulation results that strongly support the use of the weighted overlapped segment averaging method for spectral estimation.
Abstract: This letter clarifies some recent misleading comments made on spectral estimation. Specific references and simulation results are given that strongly support the use of the weighted overlapped segment averaging method for spectral estimation.

31 citations

Proceedings ArticleDOI
04 Sep 2006
TL;DR: A new spectral analysis technique is devised to combine the features of both uniform and non-uniform signal processing chains in order to obtain a good spectrum quality with low computational complexity.
Abstract: This work is a part of a drastic revolution in the classical signal processing chain required in mobile systems. The system must be low power as it is powered by a battery. Thus a signal driven sampling scheme based on level crossing is adopted, delivering non-uniformly spaced out in time sampled points. In order to analyse the non-uniformly sampled signal obtained at the output of this sampling scheme a new spectral analysis technique is devised. The idea is to combine the features of both uniform and non-uniform signal processing chains in order to obtain a good spectrum quality with low computational complexity. The comparison of the proposed technique with General Discrete Fourier transform and Lomb's algorithm shows significant improvements in terms of spectrum quality and computational complexity.

31 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202316
202248
202159
2020101
201994
201895