一种改进的可观测性的行列式方法及其度分析

Lu Jiazhen, Xie Lili, Zhang Chunxi, Wang Yan
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引用次数: 3

摘要

在可观察性分析中有几种常用的方法,如行列式法。众所周知,传统的行列式方法可以找到可观测和不可观测状态变量之间的线性依赖关系。但是,没有一种单一的方法可以完全解决可观测性中常见的困难。本文提出了一种改进的行列式方法来解决这一问题。本文表明,基于可观测状态变量与不可观测状态变量之间的线性依赖关系,可以通过建立信息矩阵来确定可观测状态变量。此外,根据所建立的信息矩阵和状态变量的初始误差协方差,通过快速评估状态变量的可观察度,可以方便地进行不可观察状态变量的最佳选择。给出了一个循序渐进的过程。仿真结果验证了该方法的有效性和优越性,并将其应用于捷联惯导系统的初始对准中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An improved determinant method of observability and its degree analysis
There are several methods applied in the observability analysis, such as determinant method. It is well known that linear dependence relationship between the observable and unobservable state variables can be found by traditional determinant method. But there is no single method which could resolve the usual difficulties in observability completely. An improved determinant method is introduced to solve this problem in this paper. It is shown here that observable state variables can be determined by establishing an information matrix based on the linear dependence relationship between observable and unobservable state variables. Also, the best choice of unobservable state variables could be performed easily by fast evaluation of observability degree based on the established information matrix and the initial error covariance of state variables. A step by step procedure is presented. Simulation results confirm the effectiveness and advantage of the new improved approach, which is applied to application of initial alignment of SINS.
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