Robust Kalman filter design for unknown noise covariance
噪声统计特性未知时的鲁棒卡尔曼滤波算法设计

This paper is concerned with the problem of a robust Kalman filter(RKF) design when noise covariance is unknown in stochastic linear systems. A novel design criterion for RKFs is proposed, and its rationality is analyzed. Based on the criterion, the design of a RKF is transformed to solving a linear matrix inequality(LMI). The results are validated by simulations.