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中文摘要：
研究了具有测量数据丢失的离散不确定时滞系统鲁棒Kalman滤波问题,其中时延存在于系统状态和观测值中.模型的不确定性通过在系统矩阵中引入随机参数扰动来表示,测量数据丢失现象则通过一个满足Bernoulli分布且统计特性已知的随机变量来描述.基于最小方差估计准则,利用射影性质和递归射影公式得到一个新的滤波器设计方法,并且保证了滤波器的维数与原系统相等.与传统的状态增广方法相比,当时延比较大时,该方法可以有效降低计算量.最后,给出一个仿真例子说明所提方法的有效性.
英文摘要：
The robust Kalman filtering problemis 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.
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