基于卡尔曼的联合估计,用于与未知不变矩阵的通用时间变量参数系统
IEEE transactions on cybernetics
|November 6, 2025
概括
本研究引入了一个新的状态空间模型,用于具有时间变化的参数的系统,提高估计准确度. 联合状态估计 (JSE) 算法减少了对先前知识的依赖,在模拟中证明可靠.
科学领域:
- 控制系统工程 控制系统工程
- 信号处理 信号处理
- 系统识别系统识别系统
背景情况:
- 对于时间变量系统的传统状态空间方法通常假定参数的马尔科夫演变,并且需要对转移矩阵的先验知识.
- 现有的方法可能受到对参数动态和系统矩阵的详细信息的需求的限制.
研究的目的:
- 为具有时间变化的参数的系统开发一种新的状态空间建模和估计方法.
- 为了减少对参数估计中不变矩阵的先前知识的依赖.
- 提高动态系统估计算法的可靠性和有效性.
主要方法:
- 为时间变化的参数开发一个明确的自回归 (AR) 模型,其中不变矩阵捕获参数动态.
- 通过将不变矩阵和时间变量的参数集成到状态向量的状态空间模型的构建.
- 基于卡尔曼过原理的联合状态估计 (JSE) 算法的推算.
主要成果:
- 数字模拟和蒙特卡洛测试证明了在各种随机白噪声条件下算法的可靠性.
- 开发的联合状态估计算法有效地估计时间变化的参数,而不需要广泛的先前知识.
- 使用实时序列数据的实际估计结果验证了拟议方法的有效性.
结论:
- 拟议的状态空间方法和联合状态估计算法为模拟和估计具有时间变化参数的系统提供了强大的方法.
- 该算法的减少对先前知识的依赖和证明的可靠性使其适合于现实世界的应用.
- 这项工作推进了对具有不断变化的特征的动态系统的系统识别技术.
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