This paper presents an adaptive Order-Statistic Filter (OSF) that can operate in the real and the complex data domains to maximize the gain in signal to noise and/or clutter ratio. This distribution-independent non-linear filter approximates the optimal filter when the background is not Gaussian (e.g., speckle-type clutter, Gamma noise, etc.), producing a "Gaussianized" residual that ensures the near-optimality of subsequent processing stages that assume Gaussian statistics (e.g., background-normalization/CFAR, signal classification, etc.). Furthermore, the residual resulting from an adaptive OSF stage can implicitly be re-filtered, driving the ensuing residuals ever closer to being Gaussian-distributed. The output of such recursive version of our adaptive OSF can thus approximate optimality in the maximum likelihood sense (e.g., in the case of signal detection, by maximizing the probability of detection while minimizing the probability of false alarm).

Date Published: 15 April 2010
PDF: 12 pages
Proc. SPIE 7698, Signal and Data Processing of Small Targets 2010, 769802 (15 April 2010); doi: 10.1117/12.848550

Published in SPIE Proceedings Vol. 7698:
Signal and Data Processing of Small Targets 2010
Oliver E. Drummond, Editor(s)