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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.


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Journal ArticleDOI
TL;DR: In this paper, the effect of weighting on the uncertainty of the discrete time Fourier transform (DTFT) samples of a signal corrupted by additive noise is investigated, and it is shown how the adopted window sequence and the autocovariance function of the noise affect the second-order stochastic moments of the frequency domain data.
Abstract: The effect of weighting on the uncertainty of the discrete time Fourier transform (DTFT) samples of a signal corrupted by additive noise is investigated. Making very weak assumptions, it is shown how the adopted window sequence and the autocovariance function of the noise affect the second-order stochastic moments of the frequency-domain data. The relationship obtained extends the results reported in the literature and is useful in many frequency-domain estimation problems. It is shown how the knowledge of the second-order moments of the transform has allowed the application of the least squares technique for the estimation of the parameters of a multifrequency signal in the frequency-domain. The estimator obtained is very useful when high-accuracy results are required under real-time constraints. The procedure exhibits a better accuracy than similar frequency-domain methods proposed in the literature. >

56 citations

Proceedings ArticleDOI
07 May 1996
TL;DR: This work addresses the narrow-band source localization problem for arbitrary arrays with known geometry in the presence of arbitrary noise of unknown spatial spectral density with a very unsophisticated approach whose algorithmic part relies on a standard linear programming algorithm.
Abstract: We address the narrow-band source localization problem for arbitrary arrays with known geometry in the presence of arbitrary noise of unknown spatial spectral density. Very few methods are able to handle this problem. We present a very unsophisticated approach whose algorithmic part relies on a standard linear programming algorithm (such as the simplex algorithm available in any scientific program library). The computational complexity of the method is reasonable, the performance appear to be remarkable on simulations. The justification of the procedure and the asymptotic analysis is more complex and much work remains to be done.

56 citations

Journal ArticleDOI
TL;DR: The frequency-domain dynamic test of analog-to-digital converters (ADCs) is considered under the assumption of noncoherent sinewave sampling and a procedure is described which is based on the windowed discrete Fourier transform (WDFT), optimized for the achievement of high estimation accuracy.
Abstract: In this paper, the frequency-domain dynamic test of analog-to-digital converters (ADCs) is considered under the assumption of noncoherent sinewave sampling. A procedure is described which is based on the windowed discrete Fourier transform (WDFT), optimized for the achievement of high estimation accuracy. With this aim, the class of windows belonging to the set of discrete prolate spheroidal sequences is adopted for the reduction of the effects of spectral leakage. Practical suggestions are given for a straightforward applicability of derived results and for an efficient estimation of ADC spectral parameters. Finally, experimental results are presented in order to validate the proposed testing approach.

56 citations

Journal ArticleDOI
TL;DR: This paper demonstrates that the proposed low-complexity method for line spectral estimation achieves estimation accuracy at least as good as current methods and that it does so while being orders of magnitudes faster.
Abstract: A number of recent works have proposed to solve the line spectral estimation problem by applying off-the-grid extensions of sparse estimation techniques These methods are preferable over classical line spectral estimation algorithms because they inherently estimate the model order However, they all have computation times that grow at least cubically in the problem size, thus limiting their practical applicability in cases with large dimensions To alleviate this issue, we propose a low-complexity method for line spectral estimation, which also draws on ideas from sparse estimation Our method is based on a Bayesian view of the problem The signal covariance matrix is shown to have Toeplitz structure, allowing superfast Toeplitz inversion to be used We demonstrate that our method achieves estimation accuracy at least as good as current methods and that it does so while being orders of magnitudes faster

56 citations

Journal ArticleDOI
TL;DR: In this article, periodogram-like and consistent estimators are proposed for spectral mass estimation when the spectral support of the process consists of lines, and detailed analysis on aliasing, bias and covariances of various estimators.
Abstract: Spectral estimation of nonstationary but harmonizable processes is considered. Given a single realization of the process, periodogram-like and consistent estimators are proposed for spectral mass estimation when the spectral support of the process consists of lines. Such a process can arise in signals of a moving source from array data or multipath signals with Doppler stretch from a single receiver. Such processes also include periodically correlated (or cyclostationary) and almost periodically correlated processes as special cases. We give detailed analysis on aliasing, bias and covariances of various estimators. It is shown that dividing a single long realization of the process into nonoverlapping subsections and then averaging periodogram-like estimates formed from each subsection will not yield meaningful results if one is estimating spectral mass with support on lines with slope not equal to 1. If the slope of a spectral support line is irrational, then spectral masses do not fold on top of each other in estimation even if the data are equally spaced. Simulation examples are given to illustrate various theoretical results.

56 citations


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