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具有多种类型的噪音和数据包损失的多传感器描述系统的强大的融合卡尔曼估计器
Jie Zheng1, Wenxia Cui1, Sian Sun1
1School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai 201620, China.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
本研究开发了一种强大的卡尔曼估计器,用于面临噪音,延迟和数据包丢失的多传感器描述系统 (MSDS). 新的估计器确保了边界误差差异,在不确定的环境中提高了系统可靠性.
科学领域:
- 控制系统工程 控制系统工程
- 信号处理 信号处理
- 估计理论 估计理论
背景情况:
- 多传感器描述系统 (MSDS) 容易受到各种干扰,包括噪音,缺失测量,时间延迟和数据包丢失.
- 这些不确定性使系统状态的准确估计变得复杂,影响系统的整体性能和可靠性.
研究的目的:
- 在复杂的噪音和数据丢失条件下开发一个强大的卡尔曼MSDS估计器.
- 为了确保估计误差差异的边界性,尽管系统的不确定性.
主要方法:
- 使用单数值分解 (SVD),增强状态和虚构噪声来转换MSDS.
- 基于min-max强大估计和卡尔曼波器理论构建一个强大的卡尔曼估计器.
- 数学归纳和利亚普诺夫方程来证明估计器的稳定性.
主要成果:
- 设计了一种新的强大的卡尔曼估计器,包括过器,预测器,光滑器和解卷组件.
- 估计器的稳定性被数学证明,保证了错误差异的上限.
- 模拟结果验证了拟议的可靠估计器的性能和有效性.
结论:
- 开发的强大的卡尔曼估计器有效地解决了MSDS估计中的挑战,这些挑战是由噪音,延迟和包丢失引起的.
- 拟议的方法为不确定和受干扰的多传感器系统的状态估计提供了可靠的解决方案.
- 保证的有限误差差在实际应用中提高了估计的可靠性.
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