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在动态系统的线性模型中研究状态变量的适应度
Maria Selezneva1, Konstantin Neusypin1, Anastasia Surkova1
1Department of Information and Control Systems (IU1), Bauman Moscow State Technical University, Moscow 105005, Russia.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了测量状态变量在估计算法的数学模型中的适应性方法. 这些技术有助于为诸如惯性导航之类的系统创建更好的模型,在不断变化的条件下提高性能.
科学领域:
- 数学建模的数学建模
- 估计算法估计算法
- 导航系统 导航系统 导航系统
背景情况:
- 数学模型中的状态变量对于估计算法至关重要.
- 了解这些变量的适应性是改善模型性能的关键.
- 惯性导航系统 (INS) 需要强大的状态变量估计.
研究的目的:
- 在数学模型中研究和量化状态变量的适应程度.
- 提出数值标准来评估线性模型中的状态变量适应性.
- 分析INS错误在动态环境条件下的适应性.
主要方法:
- 开发两个数值标准来计算状态变量的适应性.
- 适应性特征的定性分析.
- 在不同的采样周期和飞行高度下模拟INS错误动态.
主要成果:
- 拟议的数值标准有效量化线性模型中的状态变量适应性.
- 证明INS错误的适应性对采样时间和飞行高度的变化很敏感.
- 了解这些敏感度可以进行有针对性的模型改进.
结论:
- 开发的方法提供了一种定量方法来评估状态变量的适应性.
- 这些发现使得能够设计具有增强适应性特性的估计算法.
- 这项研究有助于更强大,更可靠的导航和估计系统.
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