Efficient numerical algorithm for steady-state Kalman covariance

A stable, quadratically convergent numerical algorithm is presented for computing the steady-state covariance and gain matrices of the Kalman filter. The method is more rapidly convergent than standard Riccati integration techniques and is easier to implement than existing eigenvalue-eigenvector algorithms. The quadratic convergence is proved analytically and illustrated by a numerical example