Adaptive Exponentially Weighted Moving Average Schemes Using a Kalrnan Filter

Two adaptive exponentially weighted moving average control schemes are proposed. The weighting coefficient is updated using a Kalman filter algorithm. The two test statistics incorporate an integral error term. Simulated average run lengths indicate the proposed schemes are sensitive to small process shifts, but do tend to ring false alarms when there is no process change. For medium and large process changes and trends their performance is comparable to that, of Lucas's combined Shewhart-CUSUM control scheme. Some application of the proposed schemes to correlated data indicate robust performance. Conclusions are drawn that the Kalman filter used to model a process together with a detection mechanism applied to the residuals closely resembles the work done in control theory.