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对于具有多步随机测量延迟和数据包丢失的非线性系统的边缘化高斯过的设计和复杂性分析
Yuxin Zhang1, Yunqi Chen2, Zhibin Yan1
1School of Science, Harbin Institute of Technology-Shenzhen, Shenzhen 518055, China.
ISA transactions
|August 20, 2025
概括
本研究引入了边缘化高斯过 (MGF),以减少随机延迟和数据包丢失的非线性系统的计算负载. MGF提供了与状态增强高斯过 (SAGF) 相似的精度,但处理时间显著缩短.
科学领域:
- 控制系统工程 控制系统工程
- 信号处理 信号处理
- 非线性动力学是一种非线性动力学.
背景情况:
- 现有的状态增强高斯过 (SAGF) 面临着高计算复杂性,随着随机测量延迟和数据包丢失的增加,由于增强状态维度的增长.
- 这种复杂性限制了SAGF在具有显著延迟步骤的系统中的实际应用.
研究的目的:
- 为具有多步随机测量延迟和数据包丢失的非线性系统开发一个计算高效的过方法.
- 为了减少与传统状态增强高斯过相关的计算负担.
主要方法:
- 在非线性过问题的增强系统中确定了分析线性子结构.
- 将边缘化技术应用于这些子结构,开发了一种新的边缘化高斯过 (MGF).
- 进行了量化计算复杂性分析,比较MGF和SAGF,使用浮点运算,假设西格玛点方法.
主要成果:
- 开发的边缘化高斯过 (MGF) 与单个原始状态相结合,与SAGF的增强状态集成不同.
- 计算复杂性分析表明,MGF的理论计算复杂性低于SAGF.
- 针对目标追踪的模拟实验证实了MGF与SAGF的同等估计准确性.
结论:
- 边缘化高斯过 (MGF) 有效地解决了非线性系统中SAGF的计算复杂性问题,具有随机延迟和数据包丢失.
- 在不影响估计准确性的情况下,MGF为SAGF提供了更有效的替代方案.
- 拟议的MGF方法提供了更短的运行时间,使其适合实时应用.
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