Paper
1 September 1995 Multichannel time-dependent detection of small targets in Gaussian and non-Gaussian clutter
Dennis M. Silva, Russell E. Warren
Author Affiliations +
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.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Dennis M. Silva and Russell E. Warren "Multichannel time-dependent detection of small targets in Gaussian and non-Gaussian clutter", Proc. SPIE 2561, Signal and Data Processing of Small Targets 1995, (1 September 1995); https://doi.org/10.1117/12.217674
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Cited by 1 scholarly publication.
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KEYWORDS
Signal to noise ratio

Data modeling

Target detection

Data analysis

Signal processing

Data processing

Detection and tracking algorithms

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