Multichannel time-dependent detection of small targets in Gaussian and non-Gaussian clutter

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.