Notes on Convergence and Modeling for the Extended Kalman Filter

作     者：Dah-Jing Jwo 

作者机构：Department of CommunicationsNavigation and Control EngineeringTaiwan Ocean UniversityKeelung202301TaiwanChina 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第77卷第11期

页      面：2137-2155页

核心收录：

学科分类：080902[工学-电路与系统] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：supported by the Ministry of Science and Technology Taiwan(Grant Number MOST 110-2221-E-019-042) 

主　　题：Kalman filter extended kalman filter convergence modeling optimization 

摘      要：The goal of this work is to provide an understanding of estimation technology for both linear and nonlinear dynamical systems.A critical analysis of both the Kalman filter(KF)and the extended Kalman filter(EKF)will be provided,along with examples to illustrate some important issues related to filtering convergence due to system modeling.A conceptual explanation of the topic with illustrative examples provided in the paper can help the readers capture the essential principles and avoid making mistakes while implementing the *** fictitious process noise to the system model assumed by the filter designers for convergence assurance is being investigated.A comparison of estimation accuracy with linear and nonlinear measurements is *** identification by the state estimation method through the augmentation of the state vector is also *** intended readers of this article may include researchers,working engineers,or engineering *** article can serve as a better understanding of the topic as well as a further connection to probability,stochastic process,and system *** lesson learned enables the readers to interpret the theory and algorithms appropriately and precisely implement the computer codes that nicely match the estimation algorithms related to the mathematical *** is especially helpful for those readers with less experience or background in optimal estimation theory,as it provides a solid foundation for further study on the theory and applications of the topic.