Adaptive Kalman filtering using stochastic approximation

A Kalman filter requires an exact knowledge of the noise covariance matrices to determine the optimal gain Kop for the filtering equations. In the absence of such prior information, an adaptive technique must be used. An approach based on stochastic approximation is presented. The steady-state gain is obtained by using a recursive algorithm that satisfies the innovations theorem.