An adaptive robust estimator for scale in contaminated distributions

We consider the problem of scale estimation when a nominal distribution is contaminated. Knowledge of the scale is necessary in many signal detection and estimation problems and poor estimates of the scale can have deleterious effects on subsequent processing. The approach considered here is based on the M-estimation concept of Huber, but employs a score function which is a linear combination of basis functions whose weights are adaptively estimated from the observations. Results suggest that this adaptivity increases robustness over static M-estimators.