序列融合过器用于对非线性多传感器系统的状态估计,具有交叉相关的噪声和数据包丢失补偿
Liguo Tan1, Yibo Wang2, Changqing Hu3
1Laboratory for Space Environment and Physical Sciences, Harbin Institute of Technology, Harbin 150001, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
本研究引入了一种针对非线性多传感器系统的新型状态估计方法,有效补偿数据包丢失和交叉相关噪声,以提高准确性.
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:
- 在非线性多传感器系统中,由于交叉相关的噪音和数据包丢失,对状态的估计具有挑战性.
- 现有的方法与噪声与非相关性和不可靠的网络传输扎,影响估计的准确性.
研究的目的:
- 为非线性多传感器系统提出一个强大的状态估计方法.
- 为了应对交叉相关的噪音和数据包丢失所带来的挑战.
- 在不可靠的网络环境中提高估计准确性.
主要方法:
- 顺序融合框架用于状态估计.
- 预测补偿机制和观察噪声估计策略用于数据更新.
- 创新分析方法和三度球形-半径立方体规则应用于过器的设计和实施.
主要成果:
- 拟议的方法有效地弥补了数据包丢失和交叉相关的噪声.
- 顺序融合方法提高了状态估计的准确性.
- 使用无变异非静止增长模型 (UNGM) 的模拟证明了算法的有效性.
结论:
- 开发的状态估计算法对非线性多传感器系统是有效和可行的.
- 该方法为在杂和不可靠的网络条件下运行的系统提供了强大的解决方案.
- 这项工作有助于改善复杂动态系统中的状态估计.
相关概念视频
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