A robust running-window detector and estimator for step-signals in contaminated Gaussian noise

Abstract

An N-point window is applied to noisy data to recover stepped signals in non-Gaussian noise. Robust measures of signal step level and noise distribution spread are used to detect sequential clusters of data points which are statistically significantly different, thereby detecting the step. Using conventional analysis-of-variance methods, but with robust parameter estimates, false alarm probabilities are set reasonably accurately, and miss probabilities and signal level estimates are shown by simulation to yield good results. Applications to Kalman filtering, seismic and well-log data, and image processing are indicated.