A Tightly Coupled Method for Indoor Vehicle Navigation Based on Smartphone IMU and BLE

被引:2
作者
Liu, Yuhong [1 ]
Wu, Chang [1 ]
Kong, Xiaotong [1 ]
Du, Xiaoqi [1 ]
You, Yuan [1 ]
机构
[1] Univ Elect Sci & Technol China UESTC, Inst Informat & Commun Engn, Chengdu 611731, Peoples R China
关键词
Accuracy; Sensors; Costs; Navigation; Noise; Mathematical models; Distance measurement; Real-time systems; Kalman filters; Heuristic algorithms; Bluetooth low energy (BLE); CFBS algorithm; indoor vehicle positioning and navigation; inertial measurement unit (IMU); tightly coupled extended Kalman filter (EKF); LOCALIZATION; TRACKING; WIFI;
D O I
10.1109/TIM.2024.3481552
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Nowadays, smartphone-based localization, which is the most common way for car drivers to navigate, has attracted considerable attention. In environments where global positioning system (GPS) signals are obstructed, such as indoor parking lots and large tunnels, smartphone-based localization methods still face challenges in accuracy and update frequency. Current methods utilize inertial measurement units (IMUs) to achieve short-term, high-accuracy position estimation. However, most smartphones are equipped with low-precision IMU due to cost constraints, leading to significant cumulative errors over time caused by sensor noise and external interference. Moreover, indoor positioning methods based on Bluetooth low energy (BLE) can achieve long-term and higher precision positioning estimation. However, due to the limitation of smartphone power consumption, real-time scanning and response of BLE cannot be achieved. BLE scans can only be returned after the scanning cycle is completed. Therefore, when the vehicle is moving at high speed, the update frequency of the BLE-based positioning method is lower. Furthermore, due to the asynchronous reception of BLE, motion distortion will occur, seriously affecting positioning accuracy. To address the above problems, we propose a tightly coupled indoor vehicle positioning and navigation algorithm based on smartphone IMU and BLE, named the CFBS (Synthesis of Coordinate transformation and Forward-Backward propagation) algorithm. The coordinate transformation algorithm converts the inertial dynamics from the smartphone to the vehicle, and the backward propagation algorithm compensates for BLE motion distortion and enhances positioning frequency. Finally, IMU bias and position estimation are achieved using a tightly coupled extended Kalman filter (EKF). Our algorithm is tested in real business scenarios to verify its effect, which effectively improves the positioning frequency and accuracy of indoor positioning services (ILBS).
引用
收藏
页数:12
相关论文
共 50 条
[41]   A DSRC Doppler/IMU/GNSS Tightly-coupled Cooperative Positioning Method for Relative Positioning in VANETs [J].
Shen, Feng ;
Cheong, Joon Wayn ;
Dempster, Andrew G. .
JOURNAL OF NAVIGATION, 2017, 70 (01) :120-136
[42]   RTK-LIO: Tightly Coupled RTK/LiDAR/Inertial Navigation System Based on Optimization Approach [J].
Wang, Rongtian ;
Zhang, Yuqi ;
Li, Tao ;
Wang, Chao ;
Wu, Qi ;
Pei, Ling ;
Zhang, Wen-An .
IEEE SENSORS JOURNAL, 2025, 25 (13) :26220-26227
[43]   Indoor Navigation Algorithm Based on a Smartphone Inertial Measurement Unit and Map Matching [J].
Kang, Taewon ;
Shin, Younghoon .
12TH INTERNATIONAL CONFERENCE ON ICT CONVERGENCE (ICTC 2021): BEYOND THE PANDEMIC ERA WITH ICT CONVERGENCE INNOVATION, 2021, :1421-1424
[44]   Smartphone-Based Indoor Visual Navigation with Leader-Follower Mode [J].
Xu, Jingao ;
Dong, Erqun ;
Ma, Qiang ;
Wu, Chenshu ;
Yang, Zheng .
ACM TRANSACTIONS ON SENSOR NETWORKS, 2021, 17 (02)
[45]   Robust Biomechanical Model-Based 3-D Indoor Localization and Tracking Method Using UWB and IMU [J].
Yoon, Paul K. ;
Zihajehzadeh, Shaghayegh ;
Kang, Bong-Soo ;
Park, Edward J. .
IEEE SENSORS JOURNAL, 2017, 17 (04) :1084-1096
[46]   Experimental Results from an Ultra-Tightly Coupled GPS/Galileo/WiFi/ZigBee/MEMS-IMU Indoor Navigation Test System Featuring Coherent Integration Times of Several Seconds [J].
Pany, Thomas ;
Winkel, Jon ;
Riedl, Bernhard ;
Niedermeier, Herbert ;
Eissfeller, Bernd ;
Woerz, Thomas ;
Schweikert, Robert ;
Lagrasta, Stefano ;
Nicolc, Riccardo ;
Lopez-Risueno, Gustavo ;
Jimenez-Banos, David .
2010 5TH ESA WORKSHOP ON SATELLITE NAVIGATION TECHNOLOGIES AND EUROPEAN WORKSHOP ON GNSS SIGNALS AND SIGNAL PROCESSING (NAVITEC), 2010,
[47]   Indoor and Outdoor Seamless Positioning Method Using UWB Enhanced Multi-Sensor Tightly-Coupled Integration [J].
Jiang, Wei ;
Cao, Zhuojian ;
Cai, Baigen ;
Li, Binghao ;
Wang, Jian .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2021, 70 (10) :10633-10645
[48]   New Optimized Algorithm with Nonlinear Filter for Ultra-Tightly Coupled Integrated Navigation System of Land Vehicle [J].
Tseng, Chien-Hao ;
Jwo, Dah-Jing ;
Chang, Chih-Wen .
CMC-COMPUTERS MATERIALS & CONTINUA, 2012, 27 (01) :23-53
[49]   Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas [J].
Gakne, Paul Verlaine ;
O'Keefe, Kyle .
SENSORS, 2018, 18 (04)
[50]   Performance Enhancement of Tightly Coupled GNSS/IMU Integration Based on Factor Graph With Robust TDCP Loop Closure [J].
Bai, Shiyu ;
Lai, Jizhou ;
Lyu, Pin ;
Cen, Yiting ;
Sun, Xin ;
Wang, Bingqing .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2024, 25 (03) :2437-2449