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Proceedings Paper

Multichannel time-dependent whitening of non-Gaussian data for weak-signal image processing
Author(s): Dennis M. Silva; Russell E. Warren; James G. Little
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Paper Abstract

We present an integrated, theoretically self-consistent approach to the optimal processing of non-Gaussian data for weak-signal image processing in multichannel correlated clutter backgrounds. Our approach combines linear predictive filtering with locally optimal detection theory to perform both intra- and interchannel whitening and soft editing within the context of Neyman-Pearson likelihood ratio processing. Whitening coefficients are estimated from a multichannel formulation of the Yule-Walker equations. Soft editing is performed by way of a nonlinear operator computed from the multichannel joint density of whitened residuals, which, in the Gaussian limit, is shown to reduce to a channel-dependent conditional mean subtraction. We assume the signal to be deterministic. In addition, we also assume that non-Gaussian departures in the data are limited in both space and time. Processing results are presented for both simulated and real data and are compared with standard Fourier-based approaches.

Paper Details

Date Published: 6 July 1994
PDF: 14 pages
Proc. SPIE 2235, Signal and Data Processing of Small Targets 1994, (6 July 1994); doi: 10.1117/12.179061
Show Author Affiliations
Dennis M. Silva, SRI International (United States)
Russell E. Warren, SRI International (United States)
James G. Little, SRI International (United States)

Published in SPIE Proceedings Vol. 2235:
Signal and Data Processing of Small Targets 1994
Oliver E. Drummond, Editor(s)

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