Topic
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 published on a yearly basis
Papers
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TL;DR: In this paper, a classification of power spectral estimates from the point of view of bank filter analysis is presented, and a modification of the so-called maximum likelihood estimate in order to obtain the resolution which corresponds to a power density estimate is presented.
58 citations
21 Apr 2003
TL;DR: In this paper, it is shown that the fractional domains exhibiting minimum and maximum support for a given signal define the limit on joint time-frequency resolution available under the FrFT.
Abstract: The fractional Fourier transform (FrFT) provides an important extension to conventional Fourier theory for the analysis and synthesis of linear chirp signals. It is a parameterised transform which can be used to provide extremely compact representations. The representation is maximally compressed when the transform parameter, /spl alpha/, is matched to the chirp rate of the input signal. Existing proofs are extended to demonstrate that the fractional Fourier transform of the Gaussian function also has Gaussian support. Furthermore, expressions are developed which allow calculation of the spread of the signal representation for a Gaussian windowed linear chirp signal in any fractional domain. Both continuous and discrete cases are considered. The fractional domains exhibiting minimum and maximum support for a given signal define the limit on joint time-frequency resolution available under the FrFT. This is equated with a restatement of the uncertainty principle for linear chirp signals and the fractional Fourier domains. The calculated values for the fractional domain support are tested empirically through comparison with the discrete transform output for a synthetic signal with known parameters. It is shown that the same expressions are appropriate for predicting the support of the ordinary Fourier transform of a Gaussian windowed linear chirp signal.
58 citations
TL;DR: The generalized, yet simple, formulation of SVM LSP problems is particularized here for three different issues: parametric spectral estimation, stability of Infinite Impulse Response filters using the gamma structure, and complex ARMA models for communication applications.
57 citations
12 May 1998
TL;DR: A full generalization is presented where both the autocorrelation function and power spectral density are defined in terms of a general basis set and a partial generalization where the density is the Fourier transform of the characteristic function but the characteristicfunction is defined in Terms of an arbitrary basis set.
Abstract: We generalize the Wiener-Khinchin theorem. A full generalization is presented where both the autocorrelation function and power spectral density are defined in terms of a general basis set. In addition, we present a partial generalization where the density is the Fourier transform of the autocorrelation function but the autocorrelation function is defined in terms of an arbitrary basis set. Both the deterministic and random cases are considered.
57 citations
TL;DR: In this article, the authors proposed a spectral algorithm for generating sets of random fields which are correlated with one another. But the algorithm is based on a discrete version of the Fourier-Stieltjes representation for multidimensional random fields.
57 citations