Pedestrian indoor navigation using foot-mounted IMU with multi-sensor data fusion

作     者：Liu, Shengkai Su, Tingli Wang, Binbin Peng, Shiyu Jin, Xuebo Bai, Yuting Dou, Chao 

作者机构：Beijing Technol & Business Univ Sch Comp & Informat Engn Beijing 100048 Peoples R China Beijing Technol & Business Univ Beijing Key Lab Big Data Technol Food Safety Beijing 100048 Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China AVIC China Aeropolytechnol Estab Ctr Qual Engn Beijing 100081 Peoples R China 

出 版 物：《INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL》 (国际建模、识别与控制杂志)

年 卷 期：2018年第30卷第4期

页      面：261-272页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：NSFC [61273002, 61673002] Beijing Natural Science Foundation Key Science and Technology Project of the Beijing Municipal Education Commission of China [KZ201510011012] 

主　　题：inertial measurement unit IMU trajectory tracking multi-sensor data fusion Kalman filter zero-velocity update ZUPT 

摘      要：As a widely used indoor navigation technology, the inertial measurement unit (IMU)-based method has caught considerate research interest. However, owing to the significant and inherent drift of the sensors, it is difficult to get the accurate trajectory for pedestrian movement estimation. In this paper, a foot-mounted IMU system was used to improve the accuracy of pedestrian trajectory, by fusing information from the multiple sensors. With the Kalman filter combined with the zero-velocity update (ZUPT) method, a reasonably accurate pedestrian trajectory was then obtained. Furthermore, some adjustable parameters were introduced to better correct the estimation of position and velocity. Effectiveness of the proposed method was well verified through the indoor experiments and the long track performance was also tested in runway verification.