协方差
上下界
非线性系统
线性化
计算机科学
有界函数
伯努利原理
数学优化
可扩展性
泰勒级数
滤波器(信号处理)
扩展卡尔曼滤波器
拓扑(电路)
卡尔曼滤波器
数学
控制理论(社会学)
数学分析
统计
物理
控制(管理)
量子力学
数据库
人工智能
组合数学
工程类
计算机视觉
航空航天工程
作者
Hossein Rezaei,Arash Farnam,Guillaume Crevecoeur
摘要
Summary This paper investigates a scalable recursive state estimation method for nonlinear two‐dimensional (2‐D) stochastic complex networks. In these networks, the switching topology is modelled by random variables generated by the Bernoulli distribution. Considering the Extended Kalman Filter structure, we employ the Taylor series expansion to handle the nonlinear functions in the model. Hence, high‐order terms emerging in the linearization process are modelled by norm‐bounded parameter uncertainties. Due to the uncertainty of such a system, we obtain an upper bound for the estimation error covariance. Furthermore, we obtain an upper bound for the cross covariance to reduce the proposed filter's computational complexity and improve scalability. Within the established setting, next, the proposed filter is designed in such a way that the presented upper bounds are minimized. Finally, a numerical example is provided to illustrate the effectiveness of the proposed state estimation algorithm.
科研通智能强力驱动
Strongly Powered by AbleSci AI