Minimum variance signal estimation with adaptive order statistic filters

The authors consider the estimation of a locally constant signal embedded in stationary noise with unknown statistics. They develop iterative algorithms, dubbed adaptive order statistic filters, designed to approximate the minimum variance unbiased order statistic estimator for the signal. The authors give conditions for convergence in the mean to the optimal estimator, discuss convergence rates, and present supporting simulations.<>