Extended Kalman Filter-based localization algorithm by edge computing in Wireless Sensor Networks  

作　　者：Inam Ullah Siyu Qian Zhixiang Deng Jong-Hyouk Lee 

机构地区：[1]College of Internet of Things(IoT)Engineering,Hohai University,Changzhou Campus,213022,China [2]Department of Computer and Information Security,Sejong University,Seoul,05006,Republic of Korea

出　　处：《Digital Communications and Networks》2021年第2期187-195,共9页数字通信与网络（英文版）

基　　金：The work of J.-H.Lee was supported by the Cross-Ministry Giga KOREA Project grant funded by the Korea Government(MSIT)(No.GK20P0400,Development of Mobile Edge Computing Platform Technology for URLLC Services).

摘　　要：The Extended Kalman Filter(EKF)has received abundant attention with the growing demands for robotic localization.The EKF algorithm is more realistic in non-linear systems,which has an autonomous white noise in both the system and the estimation model.Also,in the field of engineering,most systems are non-linear.Therefore,the EKF attracts more attention than the Kalman Filter(KF).In this paper,we propose an EKF-based localization algorithm by edge computing,and a mobile robot is used to update its location concerning the landmark.This localization algorithm aims to achieve a high level of accuracy and wider coverage.The proposed algorithm is helpful for the research related to the use of EKF localization algorithms.Simulation results demonstrate that,under the situations presented in the paper,the proposed localization algorithm is more accurate compared with the current state-of-the-art localization algorithms.

关 键 词：Extended Kalman filter Edge computing Kalman filter LOCALIZATION Robots State estimation 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置] TN929.5[自动化与计算机技术—控制科学与工程]