一个新的动态异常值强大的卡尔曼波器与移动地平线估计
1Department of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, 150001, China.
ISA transactions
|May 29, 2024
概括
本研究介绍了一种新的卡尔曼波器 (KF) 增强,以有效处理动态异常值. 改进过器在状态估计方面表现出卓越的稳定性和准确性,性能优于现有方法.
科学领域:
- 控制系统工程 控制系统工程
- 信号处理 信号处理
- 数据科学数据科学数据科学
背景情况:
- 动态异常值对标准卡尔曼波器 (KF) 的性能构成重大挑战.
- 准确的状态估计在各种动态系统中至关重要,但通常会受到意外数据偏差的影响.
研究的目的:
- 开发一种创新的过方法,增强对动态异常值的稳定性.
- 在存在测量异常的情况下,提高状态估计的准确性和适应性.
主要方法:
- 通过分析测量信息,开发了一种识别状态和测量动态异常值的方法.
- 使用高斯 - 学生的t混合分布 (GSTM) 模拟的噪声,参数通过变化贝叶斯 (VB) 方法推断.
- 将GSTM噪声模型集成到移动地平线估计 (MHE) 框架中.
- 通过模拟实验确定最佳窗口大小,优化估计准确度.
主要成果:
- 拟议的过器显著提高了系统适应动态变化的能力.
- 集成的GSTM噪声模型提高了MHE框架内的噪声建模的灵活性和准确性.
- 与现有过器相比,模拟结果证实了过器在抵御动态异常值方面具有卓越的稳定性.
结论:
- 拟议的过器有效地解决了KF应用中的动态异常值的挑战.
- 先进的异常值检测,灵活的噪声建模和MHE的结合提供了增强的状态估计性能.
- 这种方法为需要在噪音和异常条件下精确状态估计的系统提供了更可靠的解决方案.
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