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Optimal Estimation for Continuous-Time Systems With Delayed Measurements

机译：时滞系统的连续时间最优估计

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摘要

This note focuses on the traditional problem of the Kalman filtering for linear continuous-time systems. Although the problem has been studied widely in the past decades, little work has been done for the time-delayed systems and some fundamental problems remain to be solved. This note proposes a new tool, namely, reorganization innovation analysis approach, to investigate the filtering problem for systems with delayed measurements. The Kalman filter is given in terms of the solution of standard Riccati equations. The performance is clearly demonstrated through analytical results and simulation. The solved problem in this note is related with some more complicated problems such as H{sub}∞ fixed-lag smoothing, H{sub}∞ control with preview and control with input delays.
机译：本文着重介绍了线性连续时间系统的卡尔曼滤波的传统问题。尽管在过去的几十年中已经对该问题进行了广泛的研究，但是对于时滞系统却几乎没有做任何工作，并且一些基本问题尚待解决。本说明提出了一种新工具，即重组创新分析方法，以研究具有延迟测量的系统的过滤问题。卡尔曼滤波器是根据标准Riccati方程的解给出的。通过分析结果和模拟可以清楚地证明其性能。本注释中解决的问题与一些更复杂的问题有关，例如H {sub}∞固定滞后平滑，具有预览的H {sub}∞控制和具有输入延迟的控制。

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