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).