摘要
状态估计一直是信息物理系统(CPS)的基本问题之一。目前,状态估计主要采用以卡尔曼滤波及其扩展形式等为代表的随机过程方法。不同于已有研究,从信息融合的角度,提出了一种基于随机有限集的状态估计方法。通过将系统状态和传感器观测分别建模为随机有限集,并采用集贝叶斯滤波器及其粒子实现迭代求解,实现了对CPS状态的精确估计。最后,仿真结果验证了所提方法的可行性。
State estimation is one of the basic problems of cyber physical system(CPS).At present,the main method used for state estimation is stochastic process method represented by Kalman filter and its extended form.Different from previous researches,a new method of state estimation based on random finite set is proposed in this paper from the perspective of information fusion.The system state and sensor observation are modeled as random finite sets,and the set Bayesian filter and its particles are used to achieve iterative solution,thus achieving accurate estimation of CPS state.Finally,the simulation results verify the feasibility of the proposed method.
作者
杨超群
张恒
何立栋
杨文
YANG Chao-qun;ZHANG Heng;HE Li-dong;YANG Wen(The 14th Research Institute of China Electronics Technology Group Corporation,Nanjing 210012,China;School of Computer Engineering,Jiangsu Ocean University,Lianyungang 222002,China;School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China;School of Information Science and Engineering,East China University of Science and Technology,Shanghai 200237,China)
出处
《控制工程》
CSCD
北大核心
2022年第8期1424-1428,共5页
Control Engineering of China
基金
国家自然科学基金面上项目(61873106,61973123,61973163)
江苏省自然科学基金面上项目(BK20171264,BK20191285)
江苏省“双创博士”项目(202030033)
浙江大学工业控制技术国家重点实验室开放课题(ICT2022B49)。
关键词
信息物理系统
状态估计
随机有限集
Cyber physical system
state estimation
random finite set
