Unsupervised learning of the Kalman filter

It is the purpose of the letter to provide a short description of a general recursive method for learning the optimal stationary Kalman filter Kopt, when the plant and measurement noise covariance kernels, denoted by Q and R, respectively, are unknown. Experimental verification that confirms the theoretical expectations is presented.