A Wi-Fi RTT/data-driven inertial navigation pedestrian indoor positioning method

doi:10.13474/j.cnki.11-2246.2024.0413

Abstract

Abstract: In pursuit of investigating pedestrian indoor positioning methods based on smartphones and enhancing the precision of indoor pedestrian localization, this paper proposes a localization system utilizing Wi-Fi RTT and IMU for the indoor positioning of pedestrians using smartphones. The method comprises three key components: ①The introduction of a Wi-Fi RTT indoor positioning method that employs extended Kalman filtering to integrate distance measurement information.②The proposition of a dead reckoning method suitable for multi-phone usage, utilizing LSTM to establish a neural network model for predicting pedestrian movement speed and heading.③The development of a fusion positioning method based on ESKF that combines Wi-Fi RTT and data-driven inertial navigation to further elevate positioning accuracy. Experimental findings illustrate that, in comparison to individual Wi-Fi RTT and data-driven inertial navigation methods, the proposed approach achieves an average improvement of 10% to 20% in positioning accuracy.

Key words: smartphones, data-driven inertial navigation, Wi-Fi RTT, PDR, fusion positioning

CLC Number:

P228

ZHOU Baoding, HU Chao, SUN Chao, LIU Xu, WU Peng, YANG Junfu. A Wi-Fi RTT/data-driven inertial navigation pedestrian indoor positioning method[J]. Bulletin of Surveying and Mapping, 2024, 0(4): 76-82.

References

[1] 陈锐志,钱隆,牛晓光,等. 基于数据与模型双驱动的音频/惯性传感器耦合定位方法[J]. 测绘学报,2022,51(7):1160-1171.
[2] ZHOU Baoding,WU Zhiqian,CHEN Zhipeng,et al. Wi-Fi RTT/encoder/INS-based robot indoor localization using smartphones[J]. IEEE Transactions on Vehicular Technology,2023,72(5): 6683-6694.
[3] YU Yue,CHEN Ruizhi,SHI Wenzhong,et al. Precise 3D indoor localization and trajectory optimization based on sparse Wi-Fi FTM anchors and built-in sensors[J]. IEEE Transactions on Vehicular Technology,2022,71(4): 4042-4056.
[4] YAN Shuo,LUO Haiyong,ZHAO Fang,et al. Wi-Fi RTT based indoor positioning with dynamic weighted multidimensional scaling[C]//Proceedings of 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN). Pisa: IEEE,2019: 1-8.
[5] GENTNER C,ULMSCHNEIDER M,KUEHNER I,et al. WiFi-RTT indoor positioning[C]//Proceedings of 2020 IEEE/ION Position,Location and Navigation Symposium (PLANS). Portland: IEEE,2020: 1029-1035.
[6] LIU Xu,ZHOU Baoding,HUANG Panpan,et al. Kalman filter-based data fusion of Wi-Fi RTT and PDR for indoor localization[J]. IEEE Sensors Journal,2021,21(6): 8479-8490.
[7] GADALETA M,ROSSI M. IDNet: smartphone-based gait recognition with convolutional neural networks[J]. Pattern Recognition,2018,74: 25-37.
[8] YAN Hang,SHAN Qi,FURUKAWA Y. RIDI: robust IMU double integration[C]//Proceedings of 2018 European Conference on Computer Vision. Cham: Springer,2018: 641-656.
[9] CHEN Changhao,LU Xiaoxuan,MARKHAM A,et al. IONet: learning to cure the curse of drift in inertial odometry[C]//Proceedings of 2018 International Conference on Artificial Intelligence(ICAI).New Orleans:[s.n.],2018.
[10] HERATH S,YAN Hang,FURUKAWA Y. RoNIN: robust neural inertial navigation in the wild: benchmark,evaluations,＆ new methods[C]//Proceedings of 2020 IEEE International Conference on Robotics and Automation (ICRA). Paris: IEEE,2020: 3146-3152.
[11] LIU Wenxin,CARUSO D,ILG E,et al. TLIO: tight learned inertial odometry[J]. IEEE Robotics and Automation Letters,2020,5(4): 5653-5660.
[12] IBRAHIM M,LIU Hansi,JAWAHAR M,et al. Verification: accuracy evaluation of Wi-Fi fine time measurements on an open platform[C]//Proceedings of the 24th Annual International Conference on Mobile Computing and Networking. New Delhi India: ACM Press,2018.
[13] ZHOU Baoding,YANG Jun,LI Qingquan. Smartphone-based activity recognition for indoor localization using a convolutional neural network[J]. Sensors,2019,19(3): 621.
[14] MELAMUD O,GOLDBERGER J,DAGAN I. Context2vec: learning generic context embedding with bidirectional LSTM[C]//Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning. Berlin: Association for Computational Linguistics,2016.
[15] HE Jingjing,SUN Changku,ZHANG Baoshang,et al. Adaptive error-state Kalman filter for attitude determination on a moving platform[J]. IEEE Transactions on Instrumentation and Measurement,1971,70: 9513110.
[16] GARCÍA-FERNÁNDEZ Á F,SVENSSON L,SÄRKKÄ S. Iterated posterior linearization smoother[J]. IEEE Transactions on Automatic Control,2017,62(4): 2056-2063.
[17] YAN Shaohu,ZHUO Yongning,WU Shiqi. An adaptive RTS threshold adjust algorithm based on minimum energy consumption in IEEE 802.11 DCF[C]//Proceedings of 2003 International Conference on Communication Technology Proceedings,2003. ICCT. Beijing: IEEE,2003: 1210-1214.
[18] HESCH J A,KOTTAS D G,BOWMAN S L,et al. Camera-IMU-based localization: observability analysis and consistency improvement[J]. International Journal of Robotics Research,2014,33(1): 182-201.
[19] HE Yijia,ZHAO Ji,GUO Yue,et al. PL-VIO: tightly-coupled monocular visual-inertial odometry using point and line features[J]. Sensors,2018,18(4): 1159.