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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
01 Sep 1982
TL;DR: A five-step method for spectral estimation that combines time and lag weighting and leads to a procedure that requires less than one-half the computations of standard methods is presented.
Abstract: This paper presents a five-step method for spectral estimation that combines time and lag weighting and leads to a procedure that requires less than one-half the computations of standard methods. The Blackman-Tukey method and the Weighted Overlapped Segment Averaging (WOSA) method (widely used in sonar and other applications) are shown to be special cases of the combined method. An analysis of the mean value of the spectral estimate leads to an unusual lag weighting that corrects for poor (for example, rectangular) time weighting and affords effective windows with very good sidelobe behavior. Several examples of achievable windows are provided. Finally, the variance of the spectral estimate of the combined method is presented and evaluated for the case of rectangular time weighting, no segment overlap, and lag reshaping. It is found that the variance is virtually as low as any technique that realizes the same frequency resolution with the same finite data record length. Furthermore, the computational costs are less than one-half those of the WOSA method, primarily because no time weighting multiplications or overlap are required.

78 citations

Journal ArticleDOI
TL;DR: A matricial Newton-type algorithm designed to solve the multivariable spectrum approximation problem is described and its global convergence is proved, and it is shown that, in the case of short observation records, this method may provide a valid alternative to standard multivariables identification techniques.
Abstract: In this paper, we first describe a matricial Newton-type algorithm designed to solve the multivariable spectrum approximation problem. We then prove its global convergence. Finally, we apply this approximation procedure to multivariate spectral estimation, and test its effectiveness through simulation. Simulation shows that, in the case of short observation records, this method may provide a valid alternative to standard multivariable identification techniques such as Matlab's PEM and Matlab's N4SID.

78 citations

Journal ArticleDOI
TL;DR: It is shown that “block convolution” is a fundamental aspect of the MWC, allowing it to successfully sample and reconstruct block-sparse (multiband) signals, and a new acquisition system for continuous-time signals whose amplitudes are block sparse is proposed.
Abstract: The random demodulator (RD) and the modulated wideband converter (MWC) are two recently proposed compressed sensing (CS) techniques for the acquisition of continuous-time spectrally sparse signals. They extend the standard CS paradigm from sampling discrete, finite dimensional signals to sampling continuous and possibly infinite dimensional ones, and thus establish the ability to capture these signals at sub-Nyquist sampling rates. The RD and the MWC have remarkably similar structures (similar block diagrams), but their reconstruction algorithms and signal models strongly differ. To date, few results exist that compare these systems, and owing to the potential impacts they could have on spectral estimation in applications like electromagnetic scanning and cognitive radio, we more fully investigate their relationship in this paper. We show that the RD and the MWC are both based on the general concept of random filtering, but employ significantly different sampling functions. We also investigate system sensitivities (or robustness) to sparse signal model assumptions. Last, we show that “block convolution” is a fundamental aspect of the MWC, allowing it to successfully sample and reconstruct block-sparse (multiband) signals. Based on this concept, we propose a new acquisition system for continuous-time signals whose amplitudes are block sparse. The paper includes detailed time and frequency domain analyses of the RD and the MWC that differ, sometimes substantially, from published results.

77 citations

Journal ArticleDOI
TL;DR: Independence of the evolution time domain size (in the terms of both: dimensionality and evolution time reached), suggests that random sampling should be used rather to design new techniques with large time domain than to accelerate standard experiments.

77 citations

Book
01 Jan 1989
TL;DR: Signals and Systems Sampled data and the Z Transform Sinusoidal Response of LSI Systems Couplets and Elementary Filters The Discrete Fourier Transform The Continuous Fourier Integral Transform Application of the Fourier transform to Digital Signal Processing Digital Filter Design Inverse Filtering and Deconvolution Spectral Factorization Power Spectral Estimation Multidimensional DSP References
Abstract: Signals and Systems Sampled Data and the Z Transform Sinusoidal Response of LSI Systems Couplets and Elementary Filters The Discrete Fourier Transform The Continuous Fourier Integral Transform Application of the Fourier Transform to Digital Signal Processing Digital Filter Design Inverse Filtering and Deconvolution Spectral Factorization Power Spectral Estimation Multidimensional DSP References

77 citations


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