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.
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01 Apr 1979
TL;DR: Simultaneous frequency and bearing estimation using 2-D spectral analysis of the space-time data array is investigated and it is shown that single-quadrant prediction can lead to severe asymmetry and bias in the estimated spectra.
Abstract: Simultaneous frequency and bearing estimation using 2-D spectral analysis of the space-time data array is investigated. The spectral estimates are generated using 2-D linear prediction. It is shown that single-quadrant prediction can lead to severe asymmetry and bias in the estimated spectra; while a certain combination of the results for two adjacent quadrants yields well-behaved spectral estimates.
61 citations
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TL;DR: In this paper, a matricial Newton-type algorithm was proposed to solve the multivariable spectrum approximation problem, and its global convergence was shown to be global convergence in the case of short observation records.
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.
61 citations
TL;DR: Using maximum entropy power spectral estimation, the estimate of the frequency of a sinusoid in white noise has been shown to be very sensitive to the initial sinusoidal phase as discussed by the authors, which can be reduced by replacing the real data by its analytic form, reducing the sampling rate by two, and employing the power spectral estimate for complex data.
Abstract: Using maximum entropy power spectral estimation, the estimate of the frequency of a sinusoid in white noise has been shown to be very sensitive to the initial sinusoidal phase. This phase dependence can be significantly reduced by replacing the real data by its analytic form, reducing the sampling rate by two, and employing the power spectral estimate for complex data.
61 citations
TL;DR: In this article, a complex-valued least squares (CLS) framework is proposed to enhance the accuracy of the smart discrete Fourier transform (SDFT) algorithms for power system frequency estimation in the presence of noise and harmonic pollution.
Abstract: A complex-valued least-squares (CLS) framework is proposed in order to enhance the accuracy of the smart discrete Fourier transform (SDFT) algorithms for power system frequency estimation in the presence of noise and harmonic pollution. It is first established that the underlying time-series relationship among the consecutive DFT fundamental components employed by the original SDFT algorithms does not hold when noises or unexpected higher order harmonics are present, resulting in suboptimal estimation performances. To eliminate these adverse effects on the frequency estimation, the degree of the relationship breakdown is next quantified via a model mismatch error vector. The CLS technique is then employed to minimize the mean-square model deviation when the SDFT voltage modelling is suboptimal. The proposed CLS-enhanced SDFT (CLS-SDFT) methods are shown to be more accurate than the original ones in heavily noisy and harmonic-distorted environments, typical scenarios in online frequency estimation. The benefits of the SDFT framework are verified by simulations for various power system conditions, as well as for real-world measurements.
61 citations
TL;DR: The authors show that near zero or Nyquist frequency this approximation is poor even for white noise and derive the exact expression of the variance in the general case of a stationary real-valued time series.
Abstract: Multitaper spectral estimation has proven very powerful as a spectral analysis method wherever the spectrum of interest is detailed and/or varies rapidly with a large dynamic range. In his original paper D.J. Thomson (1982) gave a simple approximation for the variance of a multitaper spectral estimate which is generally adequate when the spectrum is slowly varying over the taper bandwidth. The authors show that near zero or Nyquist frequency this approximation is poor even for white noise and derive the exact expression of the variance in the general case of a stationary real-valued time series. This expression is illustrated on an autoregressive time series and a convenient computational approach outlined. It is shown that this multitaper variance expression for real-valued processes is not derivable as a special case of the multitaper variance for complex-valued, circularly symmetric processes, as previously suggested in the literature. >
61 citations