6 July 1994 Multichannel time-dependent whitening of non-Gaussian data for weak-signal image processing
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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.
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Dennis M. Silva, Russell E. Warren, James G. Little, "Multichannel time-dependent whitening of non-Gaussian data for weak-signal image processing", Proc. SPIE 2235, Signal and Data Processing of Small Targets 1994, (6 July 1994); doi: 10.1117/12.179061; https://doi.org/10.1117/12.179061
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