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.

Power System Frequency Estimation by Reduction of Noise Using Three Digital Filters

Abstract: Accurate estimation of power system frequency is essential for monitoring and operation of the smart grid. Traditionally, this has been done using discrete Fourier transform (DFT) coefficients of the positive fundamental frequency. Such DFT-based frequency estimation has been used successfully in phasor measurement units and frequency disturbance recorders in North America. Frequency errors in DFT-based algorithms for single-phase signals arise mainly due to noise and the leakage effect of the negative fundamental frequency. In this paper, a DFT-based frequency estimation algorithm is proposed to introduce three digital filters for reduction of estimate error due to noise and the leakage effect. This algorithm calculates the frequency estimate from the magnitude ratios of DFT coefficients to avoid the leakage effect. It compensates the estimate error, which is induced from the DFT magnitude ratios of three filtered outputs. The enhancement of signal-to-noise ratios is verified through simulations.

A virtual instrument for time-frequency analysis

Abstract: A virtual instrument for time-frequency analysis is presented. Its realization is based on an order recursive approach to the time-frequency signal analysis. Starting from the short time Fourier transform and using the S-method, a distribution having the auto-terms concentrated as high as in the Wigner distribution, without cross-terms, may be obtained. The same relation is used in a recursive manner to produce higher order time-frequency representations without cross-terms. Thus, the introduction of this new virtual instrument for time-frequency analysis may be of help to the scientists and practitioners in signal analysis. Application of the instrument is demonstrated on several simulated and real data examples.

Spectral-domain covariance estimation with a priori knowledge

Abstract: A knowledge-aided spectral-domain approach to estimating the interference covariance matrix used in space-time adaptive processing (STAP) is proposed. Prior knowledge of the range-Doppler clutter scene is used to identify geographic regions with homogeneous scattering statistics. Then, minimum-variance spectral estimation is used to arrive at a spectral-domain clutter estimate. Finally, space-time steering vectors are used to transform the spectral-domain estimate into a data-domain estimate of the clutter covariance matrix. The proposed technique is compared with ideal performance and to the fast maximum likelihood technique using simulated results. An investigation of the performance degradation that can occur due to various inaccurate knowledge assumptions is also presented

An industrially useful means for decomposition and differentiation of harmonic components of periodic waveforms

Abstract: This paper presents efficient methods to estimate the spectral content of (noisy) periodic waveforms that are common in industrial processes The techniques presented, which are based on the recursive discrete Fourier transform, are especially useful in computing high-order derivatives of such waveforms Unlike conventional differentiating techniques, the methods presented differentiate in the frequency domain and thus are quite immune to uncorrelated measurement noise This paper also shows the theoretical relationship between the proposed methods and those of well-known resonant filters

Asymptotic equivalence of spectral density estimation and Gaussian white noise

Abstract: We consider the statistical experiment given by a sample y(1 ), ... , y(n) of a stationary Gaussian process with an unknown smooth spectral density f . Asymptotic equivalence, in the sense of Le Cam's deficiency Δ-distance, to two Gaussian experiments with simpler structure is established. The first one is given by independent zero mean Gaussians with variance approximately f (ω i ), where ω i is a uniform grid of points in (-π, π) (nonparametric Gaussian scale regression). This approximation is closely related to well-known asymptotic independence results for the periodogram and corresponding inference methods. The second asymptotic equivalence is to a Gaussian white noise model where the drift function is the log-spectral density. This represents the step from a Gaussian scale model to a location model, and also has a counterpart in established inference methods, that is, log-periodogram regression. The problem of simple explicit equivalence maps (Markov kernels), allowing to directly carry over inference, appears in this context but is not solved here.