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Proceedings Paper

Evolutionary spectral estimation based on adaptive use of weighted norms
Author(s): Sergio D. Cabrera; Benjamin C. Flores; Gabriel Thomas; Javier Vega-Pineda
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Paper Abstract

In this paper, an evolutionary spectral estimator based on the application of Adaptive Weighted Norm Extrapolation (AWNE) is formulated and illustrated for analysis of nonstationary signals. The AWNE method produces a stationary extension of the data so that computing its Fourier transform yields a nonparametric, high-resolution spectrum estimate. The evolutionary formulation described here uses a time slice of the time-averaged Spectrogram to select the initial weight function (prior spectrum) used in AWNE for each block of data. This function strongly influences the final shape of the resulting spectrum. The resulting Short-Time AWNE (STAWNE) time-frequency representation yields improved frequency-domain resolution, preserves components which last longer than one time block, and is devoid of cross-terms. Comparison with short-time autoregressive spectral estimation yields improved consistency in the spectral energy levels as time varies. Finally, this sequential spectrum estimator is also illustrated for use in range-Doppler imaging of reflectivity surfaces having prominent scatterers by hybrid two-dimensional spectral estimation in-tandem with the discrete Fourier transform.

Paper Details

Date Published: 1 November 1993
PDF: 12 pages
Proc. SPIE 2027, Advanced Signal Processing Algorithms, Architectures, and Implementations IV, (1 November 1993); doi: 10.1117/12.160432
Show Author Affiliations
Sergio D. Cabrera, Univ. of Texas/El Paso (United States)
Benjamin C. Flores, Univ. of Texas/El Paso (United States)
Gabriel Thomas, Univ. of Texas/El Paso (United States)
Javier Vega-Pineda, Univ. of Texas/El Paso (United States)


Published in SPIE Proceedings Vol. 2027:
Advanced Signal Processing Algorithms, Architectures, and Implementations IV
Franklin T. Luk, Editor(s)

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