On signal recovery with adaptive order statistic filters

Adaptive order statistic filters are used to estimate a constant amplitude signal embedded in noise with unknown statistics. Iterative algorithms are proposed which adapt the order statistic filter to minimize the mean-square estimation error, both with and without an unbiasedness constraint. For each case, the algorithm used is shown to achieve convergence in the mean to the optimal filter. Properties of the convergence rates are discussed, and conditions for convergence in mean square are noted.<
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