Robust Kalman Filtering for Uncertain Discrete Time-delay Systems with Missing Measurement
具有测量数据丢失的离散不确定时滞系统鲁棒Kalman滤波

The robust Kalman filtering problem is investigated in this paper for linear uncertain stochastic systems with state delay, observation delay, and missing measurement. For robust performance, stochastic parameter perturbations are considered in the system matrix. The missing measurement can be described by a Bernoulli distributed random variable and its probability is assumed to be known. Based on the minimum mean square error (MMSE) estimation principle, a new filter design method is proposed by using the projection theory. The dimension of the designed filter is the same as the original systems. Compared with conventional state augmentation, the presented approach greatly lessens the computational demand when the delay is large. A simulation example is given to illustrate the effectiveness of the proposed approach.