A new autocovariance least-squares method for estimating noise covariances

Industrial implementation of model-based control methods, such as model predictive control, is often complicated by the lack of knowledge about the disturbances entering the system. In this paper, we present a new method (constrained ALS) to estimate the variances of the disturbances entering the process using routine operating data. A variety of methods have been proposed to solve this problem. Of note, we compare ALS to the classic approach presented in Mehra [(1970). On the identification of variances and adaptive Kalman filtering. IEEE Transactions on Automatic Control, 15(12), 175–184]. This classic method, and those based on it, use a three-step procedure to compute the covariances. The method presented in this paper is a one-step procedure, which yields covariance estimates with lower variance on all examples tested. The formulation used in this paper provides necessary and sufficient conditions for uniqueness of the estimated covariances, previously not available in the literature. We show that the estimated covariances are unbiased and converge to the true values with increasing sample size. The proposed method also guarantees positive semidefinite covariance estimates by adding constraints to the ALS problem. The resulting convex program can be solved efficiently.