不确定无序量测系统的最优网络化状态估计

Optimal Networked State Estimation for Out-of-Sequence Measurement System with Uncertain Delay

本文以线性时不变或系统参数预先确定的单传感器系统为对象,研究带有不确定随机无序量测约束下的最优网络化估计问题.基于线性时不变或参数预先确定系统的滤波器系数矩阵离线计算和线性最小方差估计的线性加权求和特性,首先介绍传统Kalman滤波的等价测量值加权求和形式.然后,以该测量值加权求和滤波器为基础,结合布尔二值开关和无序量测直接求和补偿技术,针对两种典型无序量测跟踪系统分别设计全局最优网络化Kalman滤波器.最后,四个仿真例子验证了本文算法的有效性和优越性.

We study the optimal networked estimation of a kind of Linear Time Invariant（LTI） or determinate parameters systems with uncertain ＂Out-Of-Sequence＂ Measurement（OOSM） induced by random communication delays.Both available properties,which are off-line computation of filter＇s parameters for these systems and linear weighted combination for the Linear Minimum Mean Square Error（LMMSE） estimate,are fully used to present the weighted summation form of the Kalman filter（KF）.Afterward,two optimal delayed filters for different uncertain OOSM systems are proposed on the basis of the linear weighted summation filter.At last,four simulation examples are demonstrated to validate the effectiveness and superiority of the proposed networked estimators.