惯性测量装置
全球定位系统
计算机科学
惯性导航系统
GPS/INS
传感器融合
卡尔曼滤波器
计量单位
保险丝(电气)
人工智能
算法
GPS信号
计算机视觉
辅助全球定位系统
工程类
数学
方向(向量空间)
电信
电气工程
物理
量子力学
几何学
作者
Zhumu Fu,Yuxuan Liu,Pengju Si,Fazhan Tao,Nan Wang
出处
期刊:IEEE Sensors Journal
[Institute of Electrical and Electronics Engineers]
日期:2023-08-15
卷期号:23 (16): 18644-18655
标识
DOI:10.1109/jsen.2023.3294508
摘要
Inertial navigation systems (INSs) often use inertial measurement units (IMUs) to produce precise results by combining them with GPS data. However, precise positioning is difficult in a GPS-denied environment. Most of the existing inertial-aided localization techniques rely on a single IMU, which can perform well when the GPS signal is good. When the GPS is not available, the IMU’s performance will rapidly deteriorate, leading to accumulating errors and positioning failure. In this article, we investigate the error distribution of the INS in the absence of GPS and present a pose estimation approach based on multiple IMU fusion using an adaptive extended Kalman filter (AEKF). Then, we propose a confidence level fusion technique that merges all IMUs into a virtual IMU to reduce redundancy and computational cost. To avoid unnecessary algorithm loss, we provide a confidence level judgment technique. According to the results, the latitude and altitude accuracy are improved by 23% and 17%, respectively, compared to the data from multiple IMUs. Compared to the method based on the law of weak majority that fuses multiple IMUs outputs, longitude and latitude are improved by 13% and 28%, respectively.
科研通智能强力驱动
Strongly Powered by AbleSci AI