Vehicle multi-source fusion navigation method based on smartphone platform

被引：0
|
作者：
Ji X. [1 ,2 ]
Wei D. [2 ]
Yuan H. [2 ]
Zhou D. [1 ]
机构：
[1] School of Electronics and Information, Northwestern Polytechnical University, Xi'an
[2] Aerospace Information Research Institute, Chinese Academy of Science, Beijing
来源：
Zhongguo Guanxing Jishu Xuebao/Journal of Chinese Inertial Technology | 2020年 / 28卷 / 05期
关键词：
Geomagnetic matching; MIMU; Multi-source fusion; Vehicle navigation;
D O I：
10.13695/j.cnki.12-1222/o3.2020.05.011
中图分类号：
学科分类号：
摘要：
For vehicle navigation applications in the GNSS denied scenarios such as dense urban canyon, parking lot and tunnel, it is quite difficult for the smartphone-based navigation scheme to maintain continuous and reliable positioning. To solve these problems, a vehicle multi-source fusion navigation method applicable for smartphone platform is proposed. With the characteristics of geomagnetic anomaly and the mileage data, a magnetic matching is performed to obtain the vehicle real-time position. Meanwhile, the GNSS information, the vehicle kinematical model and the wheel speed information from On-Board Diagnostic (OBD) system are utilized to fuse with the MIMU-SINS results and then restrain the error divergence further effectively. Road tests showed that the proposed method could provide accurate and continuous position. The horizontal error and the elevation positioning error is 2.4 m and 0.7 m respectively, which can meet the demands of urban vehicle navigation applications well. © 2020, Editorial Department of Journal of Chinese Inertial Technology. All right reserved.
引用
收藏
页码：638 / 644and693
相关论文
共 12 条
  • [1] Benedetti E, Dermanis A, Crespi M., On the feasibility to integrate low-cost MEMS accelerometers and GNSS receivers, Advances in Space Research, 59, 11, pp. 2764-2778, (2017)
  • [2] Shin Eun-Hwan, Estimation Techniques for low-cost inertial navigation, (2005)
  • [3] Li L, Chen J, Yang L, Et al., Intelligent map-matching algorithm based on map information, Journal of Chinese Inertial Technology, 24, 2, pp. 170-174, (2016)
  • [4] Cheng X, Zhao Y, Tian Y, Et al., Inertial navigation algorithm aided by motion constraints of vehicle, Journal of Chinese Inertial Technology, 27, pp. 295-300, (2019)
  • [5] Xu J, Xiong Z, Liu J, Et al., Vehicle integrated navigation algorithm based on kinematical model for intelligent mobile phone platform, Journal of Chinese Inertial Technology, 25, 2, pp. 203-208, (2017)
  • [6] Zhou G, Wang Q, Gao Y., Pedestrian navigation algorithm based on ZIHR heading angle correction method, Systems Engineering and Electronics, 41, 1, pp. 175-182, (2019)
  • [7] Choi Kyoungtaek, Suhr Jae Kyu, Jung Gi, Map-matching-based cascade landmark detection and vehicle localization, IEEE Access, 7, pp. 127874-127894, (2019)
  • [8] Muhamad Ilyas, Sangdeok Park, Seung-Ho Baeg, Improving unmanned ground vehicle navigation by exploiting virtual sensors and multi sensor fusion, Journal of Mechanical Science and Technology, 28, 11, pp. 4369-4379, (2014)
  • [9] Yan W, Bastos Luisa, Magalhaes Americo, Performance assessment of the android smartphone's IMU in a GNSS/INS coupled navigation model, IEEE Access, 7, pp. 171073-171083, (2019)
  • [10] Wei D, Huang L, Ji X, Et al., Feasibility analysis of magnetic navigation for vehicles, Sensors, 19, (2019)
12