Open AccessReliable Personnel Positioning in Industrial Environments Based on Improved Adaptive EKF With Random Packet Loss
Author(s) -
Xiaoyan Wang,
Wenyan Wang,
Xuejiao Bai
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1865/4/042033
Subject(s) - extended kalman filter , computer science , packet loss , robustness (evolution) , estimator , network packet , kalman filter , covariance , covariance matrix , real time computing , control theory (sociology) , algorithm , artificial intelligence , mathematics , computer network , statistics , biochemistry , chemistry , control (management) , gene
In the complex industrial environment, random packet loss may occur in the process of sensor data transmission. The traditional Extended Kalman Filter (EKF) algorithm will reduce the estimation accuracy, even lead to the divergence of the estimator. To solve these problems, an improved adaptive extended Kalman filter (IAEKF) is proposed to estimate the covariance matrix of process noise adaptively. At the same time, the forgetting factor of strong tracking filter (STF) is introduced to improve the robustness of the algorithm in the case of random packet loss. Simulation results show that IAEKF algorithm can effectively reduce the personnel positioning error in the case of random packet loss, and has better localization accuracy, meeting the requirements of industrial environment.