A SVD-based transient error method for analyzing noisy multicomponent exponential signals
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Date
1987-04-06
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
The problem of estimating the parameters of noisy multicomponent signals using
parametric modeling technique is considered in this paper. The multicomponent signal of
interest is formed by a superposition of basic functions having the same location in time
but different widths and amplitudes. Based on the modified Gardner transformation, some
samples of deconvolved data are derived from the multicomponent signals. The
deconvolved data are then modeled using a special nonstationary autoregressive moving
average (ARMA) process in which the parameters of the ARMA model are obtained by
linear least-squares procedure. The least-squares procedure is based on the singular
value decomposition (SVD) to overcome the limitations of the transient error method
(TEM) of analysis that uses cholesky decomposition to determine its AR coefficients. The
moving average (MA) coefficients corresponds to the initial residual error sequences so
as to account for the nonstationary noise in the deconvolved data. This new method of
analysis, termed the SVD-based transient error method, produces high resolution
estimates of the exponents of multicomponent signals at both low and high signal to noise
(SNR) ratios.
Description
Keywords
Transient analysis, Error analysis, Signal analysis, Parameter estimation, Signal resolution, Autoregressive processes, Signal to noise ratio, Magnetic analysis, Shape, Frequency
Citation
Salami, M., Nichols, S., & Smith, M. (1987, April). A SVD-based transient error method for analyzing noisy multicomponent exponential signals. In ICASSP'87. IEEE International Conference on Acoustics, Speech, and Signal Processing (Vol. 12, pp. 677-680). IEEE.