A filter for on-line estimation of spectral content

A robust filter algorithm to extract, a posteriori, the rational signal model from a noisy measurement, with little a priori information, is proposed. The spectrum and the statistics of the signal and of the corrupting noise are assumed unknown, except that the signal is assumed to have a rational s...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 1999-12, Vol.48 (6), p.1047-1055
Main Authors: Mallory, G.J.W., Doraiswami, R.
Format: Article
Language:English
Subjects:
Algorithms
Atmospherics
Data mining
Density
Density measurement
Direct current
Extraction
Frequency estimation
Frequency measurement
Information filtering
Information filters
Instrumentation
Noise robustness
Signal processing
Signal processing algorithms
Signal to noise ratio
Statistics
Vegetation
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Summary:A robust filter algorithm to extract, a posteriori, the rational signal model from a noisy measurement, with little a priori information, is proposed. The spectrum and the statistics of the signal and of the corrupting noise are assumed unknown, except that the signal is assumed to have a rational spectrum. An algorithm based on system and signal theory is derived to select a set of frequencies where the signal-to-noise ratio (SNR) is high from a given measurement spectrum. The density of selected frequencies weights the importance of the measurement as a function of frequency, An estimate of the signal model is obtained from the best weighted least-squares fit to the measurement spectrum at the selected frequencies. The proposed filter has applications to control and signal processing, and a wide variety of applications are presented. Applications include: system identification of a dc motor and a two-link manipulator, extraction of a myo-electric signal from a noisy measurement, the assignment of a rational model to a vegetation tissue's impedance, and to the number density profile of atmospheric oxygen.
ISSN:0018-9456
1557-9662
DOI:10.1109/19.816112