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Robustness to outliers is often a desirable property of statisticalestimators. Indeed many well known estimators offer very good optimalperformance in theory but are unusable in applied contexts because of theirsensitivity to outliers. Of particular interest to the authors is the case ofcovariance estimators in adaptive matched filtering schemes in signalprocessing applications such as RADAR and SONAR detection, for which acontamination by outliers of the estimated noise covariance can lead to a greatimpact on performances, in particular when these outliers are similar to thetarget signal of the matched filter.  This paper presents a generic method for building partial estimators fromknown estimators, which aim at avoiding these issues; the resulting algorithmsare shown for a few chosen cases.

Partial estimators and application to covariance estimation of gaussian and elliptical distributions