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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TL;DR: Performance of each of the techniques, in terms of bias and variance, in the presence of noise is studied and the results are compared to those of the Cramer?Rao Bound.
53 citations
TL;DR: The maximum entropy method (MEM) is applied to the interferogram data obtained using the technique of Fourier transform spectroscopy for estimating its spectrum with a resolution far exceeding the value set by the spectrometer.
Abstract: The maximum entropy method (MEM) is applied to the interferogram data obtained using the technique of Fourier transform spectroscopy for estimating its spectrum with a resolution far exceeding the value set by the spectrometer. For emission line data, the MEM process is directly used with the interferogram data in place of the regular Fourier transformation process required in Fourier transform spectroscopy. It produces a spectral estimate with an enhanced resolution. For absorption data with a broad background spectrum, the method is applied to a modified interferogram which corresponds to the Fourier transform of the absorptance spectrum. Two results are presented to demonstrate the power of the technique: for the visible emission spectrum of a spectral, calibration lamp and for the infrared chloroform absorption spectrum. Included in the paper is a discussion of the problems associated with practical use of the MEM.
53 citations
TL;DR: Autoregressive modeling has been found to give better results when analysing small sample volumes obtained from a pulsed velocimeter (narrow spectrum), even for short data lengths.
Abstract: The spectral analysis of Doppler blood flow velocity signals enjoys wide-spread interest owing to the exhaustive information on the signal which it yields. The discrete Fourier transform is the most extensively used method of analysis. However, the statistical stability of such analysis is poor; spectral smoothing, which improves the statistical stability, also results in greater width and poorer resolution of the spectrum. Autoregressive modelling has been found to give better results when analysing small sample volumes obtained from a pulsed velocimeter (narrow spectrum), even for short data lengths.
53 citations
TL;DR: A method of estimating time-varying spectra of nonstationary signals using recursive least squares (RLS) with variable forgetting factors (VFFs) is described, which has better adaptability than the conventional algorithm with high fixed forgetting factor (FFF) in the non stationary situation, and has lower variance than theventional one with low FFF in the stationary situation.
Abstract: A method of estimating time-varying spectra of nonstationary signals using recursive least squares (RLS) with variable forgetting factors (VFFs) is described. The VFF is adapted to a nonstationary signal by an extended prediction error criterion which accounts for the nonstationarity of the signal. This method has better adaptability than the conventional algorithm with high fixed forgetting factor (FFF) in the nonstationary situation, and has lower variance than the conventional one with low FFF in the stationary situation. The extra computation time for the forgetting adaptation is almost negligible. >
52 citations
TL;DR: A new method for on-line spectral estimation of nonstationary time series via autoregressive (AR) model construction is proposed and demonstrated by computer simulation study and applying to the actual data of electroencephalogram (EEG).
Abstract: A new method for on-line spectral estimation of nonstationary time series via autoregressive (AR) model construction is proposed. The method consists of on-line parameter estimation based on the recursive least squares ladder estimation algorithm with a forgetting factor and on-line order determination based on AIC with some modifications. The effectiveness of the proposed method is demonstrated by computer simulation study and applying to the actual data of electroencephalogram (EEG). >
52 citations