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Proceedings Paper

Multichannel time-dependent detection of small targets in Gaussian and non-Gaussian clutter
Author(s): Dennis M. Silva; Russell E. Warren
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Paper Abstract

Small-target (single/sub-pixel) detection techniques that use non-Fourier-based whitening approaches are presented for inputs consisting of a time series of images in one or more data channels. Backgrounds are assumed to be complicated by spatial correlation (Gaussian clutter) that is further correlated over time and across channels and may be further corrupted by highly localized non-Gaussian interference terms (`spikes') that appear target-like. For signals of known shape in Gaussian clutter, the Neyman-Pearson criterion leads to an optimal test that employs a self-consistent whitening approach based upon a time-dependent, multichannel linear predictive filtering kernel estimated from the data via least squares. Additionally, an adaptation of iterative scaling is shown to be an effective tool for partitioning correlated and uncorrelated elements of a time series of images. The partitioning of correlated from uncorrelated data, in turn, leads to an approach for isolating targets in Gaussian clutter corrupted by random spikes or for editing spikes in Gaussian clutter without affecting correlated signals or `punching holes' in correlated backgrounds. When possible, results are compared to theoretical predictions and/or optimal processing.

Paper Details

Date Published: 1 September 1995
PDF: 12 pages
Proc. SPIE 2561, Signal and Data Processing of Small Targets 1995, (1 September 1995); doi: 10.1117/12.217674
Show Author Affiliations
Dennis M. Silva, SRI International (United States)
Russell E. Warren, SRI International (United States)


Published in SPIE Proceedings Vol. 2561:
Signal and Data Processing of Small Targets 1995
Oliver E. Drummond, Editor(s)

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