Estimation of unknown noise covariances in a Kalman filter is a problem of significant practical interest in a wide array of applications. This paper presents a single-pass stochastic gradient descent (SGD) algorithm for noise covariance estimation for use in adaptive Kalman filters applied to non-stationary systems where the noise covariances can occasionally jump up or down by an unknown magnitude. Unlike our previous batch method or our multi-pass decision-directed algorithm, the proposed streaming algorithm reads measurement data exactly once and has similar root mean square error (RMSE). The computational efficiency of the new algorithm stems from its one-pass nature, recursive fading memory estimation of the sample cross-correlations of the innovations, and the RMSprop accelerated SGD algorithm. The comparative evaluation of the proposed method on a number of test cases demonstrates its computational efficiency and accuracy.

Estimation of unknown noise covariances in a Kalman filter is a problem of significant practical interest in a wide array of applications. Although this problem has a long history, reliable algorithms for their estimation were scant, and necessary and sufficient conditions for identifiability of the covariances were in dispute until recently. Necessary and sufficient conditions for covariance estimation and a batch estimation algorithm. This paper presents stochastic gradient descent (SGD) algorithms for noise covariance estimation in adaptive Kalman filters that are an order of magnitude faster than the batch method for similar or better root mean square error (RMSE) and are applicable to non-stationary systems where the noise covariances can occasionally jump up or down by an unknown magnitude. The computational efficiency of the new algorithm stems from adaptive thresholds for convergence, recursive fading memory estimation of the sample cross-correlations of the innovations, and accelerated SGD algorithms. The comparative evaluation of the proposed method on a number of test cases demonstrates its computational efficiency and accuracy.