%0 Journal Article
%T Cram¨¦r-Rao Bounds for Estimation of Linear System Noise Covariances
%J Journal of Mechanical Engineering and Automation
%@ 2163-2413
%D 2012
%I 
%R 10.5923/j.jmea.20120202.02
%X The performance of Kalman filter depends directly on the noise covariances, which are usually not known and need to be estimated. Several estimation algorithms have been published in past decades, but the measure of estimation quality is missing. The Cram¨¦r-Rao bounds represent limitation of quality of parameter estimation that can be obtained from given data. In this article, The Cram¨¦r-Rao bounds for noise covariance estimation of linear time-invariant stochastic system will be derived. Two different linear system models will be considered. Further, the performance of earlier published methods will be discussed according to the Cram¨¦r-Rao bounds. The analogy between the Cram¨¦r-Rao bounds and the Riccati equation will be pointed out.
%K Cram¨¦r-Rao bounds
%K Kalman Filter
%K Noise covariance estimation
%U http://article.sapub.org/10.5923.j.jmea.20120202.02.html