Accurate and Direct GNSS/PDR Integration Using Extended Kalman Filter for Pedestrian Smartphone Navigation
https://doi.org/10.17285/0869-7035.0034
Abstract
According to well-described literature concerning the work history of multipath mitigation in the global navigation satellite systems (GNSS), multipath is still the most dominant factor in a challenging environment. There are unperturbed harsh circumstances where GNSS signals cannot reach and smartphone navigation is not possible. The main objective of this research is to find an accurate solution for pedestrian smartphone navigation in a multipath environment. Experiments are done with micro-electro-mechanical system (MEMS) sensors mounted on a smartphone, and no extra hardware is needed. The latest Android smartphone is used to log the data files of GNSS and MEMS sensors. This scheme has been classified in the synopsis, and a rectangular route with three perpendicular turns has been selected for a pedestrian walk. The data is preprocessed using a low pass filter to remove high-frequency noise and smooth the signal. The description of accumulative error produced by the heading and step size estimation has been reduced by implementing the indices of mean cumulative heading error and cumulative step length error, respectively. In the end, the suboptimal extended Kalman filter algorithm is used to fuse the data of GNSS and pedestrian dead reckoning (PDR) for final results. In this paper, we try to give a technique to provide accurate pedestrian smartphone navigation. The fusion results show that the prospective method explores the possibility to use smartphone navigation in any case when GNSS or PDR information is not available. Substantial simulations are implemented and corroborate that the schemed method is sturdier to use in a harsh environments. The aim is to achieve high-level accuracy with an ultra-low-cost solution.
About the Authors
A. Rehman
Institute for Sensing and Navigation, Shanghai Jiao Tong University; Shanghai Key Laboratory of Navigation and Location-based Services, Shanghai Jiao Tong University, China
China
China
H. Shahid
Department of Electronic Engineering, Fudan University, China
China
China
М. А. Афзал
Shanghai Key Laboratory of Navigation and Location-based Services, Shanghai Jiao Tong University, China
China
China
H.M.A. Bhatti
Department of Computer Science and Technology, Donghua University, China
China
China
References
1. Bhuiyan, M.Z.H., and Lohan, E.S., Multipath mitigation techniques for satellite-based positioning applications, Global Navigation Satellite Systems: Signal, Theory and Applications, InTech Publisher, 2012.
2. Braasch, M.S., Multipath effects, Global Positioning System: Theory and Applications, Volume 1, Eds. B.W. Parkinson and J.J. Spiker Jr., American Institute of Aeronautics and Astronautics, Inc., 1996, pp. 547–566.
3. Chen, K.Y., Janz, K.F., Zhu, W., and Brychta, R.J., Redefining the roles of sensors in objective physical activity monitoring, Medicine and Science in Sports and Exercise, 2012, vol. 44, no. 15, pp. 513–523.
4. McGrath, M.J., Ni Scanaill, C., and Nafus, D., Sensor Technologies: Healthcare, Wellness, and Environmental Applications, Apress Open Publisher, 2013.
5. Tian, Q., Salcic, Z., Wang, K.I-K., and Pan, Y., A multi-mode dead reckoning system for pedestrian tracking using smartphone, IEEE Sensors Journal, 2016, vol. 16, no. 7, pp. 2079–2093.
6. Yao, Y., Xu, X., Zhu, C., Zhu, C., and Chan, C.-Y., A hybrid fusion algorithm for GPS/INS integration during GPS outages, Measurement, 2017, vol. 103, pp. 42–51.
7. Van Dierendonck, J., Fenton, P.J., and Ford, T.J., Theory and performance of narrow correlator spacing in GPS receiver, Journal of the Institute of Navigation, 1992, vol.39, no. 3, pp. 265–283.
8. Wang, G. et al., A GNSS/INS integrated navigation algorithm based on Kalman filter, IFACPapersOnLine, 2018, vol. 51, no. 17, pp. 232–237.
9. Qi, H., and Moore, J.B., Direct Kalman filtering approach for GPS/INS integration, IEEE Transactions on Aerospace and Electronic Systems, 2002, vol. 38, no. 2, pp. 687–693.
10. Quan, W., Li, J., Gong, X., and Fang, J., INS/CNC/GNSS Integrated Navigation Technology, Beijing: National Defense Industrial Press, 2011.
11. Yang, B., Qin, Y.-Y., and Chai, Y., Application of UKF in direct method of Kalman filter for INS/GPS, Chinese Journal of Sensors and Actuators, 2007, vol. 20, no. 4, pp. 842–846.
12. Arribas, J., Moragrega, A., Fernandez-Predes, C., and Closas, P., Low-cost GNSS/INS/odometric sensor fusion platform for ground intelligent transportation systems, Proc. 30th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 2017) Portland, Oregon, 2017.
13. Khanh, N.-H., Lee, K.H., and Lee, S.-W., An indoor positioning system using pedestrian dead reckoning with WiFi and map-matching aided, Proc. 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2017.
14. Bolad, U., and Akcakoca, M., A hybrid indoor positioning solution based on WiFi, magnetic field, and inertial navigation, Proc. 14th Workshop on Positioning, Navigation and Communication (WPNC), 2017.
15. Zou, H., Chen, Z., and Spanos, C.J., Accurate indoor localization and tracking using smartphone inertial sensors, WiFi and iBeacon, Proc. 2017 IEEE International Symposium on Inertial Sensors anв Systems (INERTIAL), 2017.
16. Chen, Q., Ding, D., and Zheng, Y., Indoor pedestrian tracking with sparse RSS fingerprins, Tsinghua Science and Technology, 2018, vol. 23, no. 1, pp. 95–103.
17. Hsu, L.-T., Gu, Y., Huang, Y., and Kamijo, S., Urban pedestrian navigation using smartphone-based dead reckoning and 3-D map-aided GNSS, IEEE Sensors Journal, 2016, vol. 16, no. 5, pp. 1281–1293.
18. Langlois, C., Tiku, S., and Pasricha, S., Indoor localization with smartphones: Harnessing the sensor suite in your pocket, IEEE Consumer Electronics Magazine, 2017, vol. 6, no. 4, pp. 70–80.
19. Groves, P., Jiang, Z., Rudi, M., and Strode, P., A portfolio approach to NLOS and multipath mitigation in dense urban areas, Proc. 2013 Institute of Navigation Conference on GNSS, Nashville, TN, USA, 2013.
20. Bullock, J.B., Foss, M., Geier, G., and King, M., Integration of GPS with other sensors and network assistance, Understanding GPS: Principles and Applications, E.D. Kaplan Ed., second edition, Mobile Communication Series, 2006, pp. 459–558.
21. Yun, X., Bachmann, E.R., and McGhee, R.B., A simplified quaternion-based algorithm for orientation estimation from earth gravity and magnetic field measurements, IEEE Transactions on Instrumentation and Measurements, 2008, vol. 57, no. 3, pp. 638–650.
22. Shin, E., Estimation techniques for low-cost inertial navigation, Ph.D. thesis, Calgary: Geomatics Eng., University of Calgary, 2005, UCGE Report 20207.
23. Groves, P.D, Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, Second edition, Artech House, 2013.
24. Liu, Q., Ying, R., Wang, Y., Qian, J., and Liu, P., Pseudorange double difference algorithm based on duty-cycled carrier phase smoothing on low-power smart devices, China Satellite Navigation Conference (CSNC) 2018 Proceedings, Lecture Notes in Electrical Engineering, Singapore: Springer, 2018, pp. 415–430.
25. Hu, G., Wang, W., Zhong, Y., Gao, B., and Gu, C., A new direct filtering approach to INS/GNSS integration, Elsevier Aerospace Science and Technology, 2018, vol. 77, pp. 755–764.
26. Rehman A., Liu, Q., Wu, Z., Zhu, H., Qian, J., Eang, Y., and Liu, P., PDR/GNSS fusion algorithm based on joint heading estimation, China Satellite Navigation Conference 2019 Proceedings, 2019, pp. 326–339.
27. Kubo, N., Kikuchi, R., and Higuchi, M., A unique approach to strong multipath mitigation in dense urban areas, Proc. 28th International Technical Meeting of the ION Satellite Division of the Institute of Navigation (ION GNSS+ 2015), Tampa, Florida, USA, 2015.
28. Farrell, J., and Givargis T., Differential GPS reference station algorithm-design and analysis, IEEE Transactions on Control Systems Technology, 2000, vol. 8, no. 3, pp. 519–531.
29. Yang, Y., Hatch, R.R., and Sharpe, R.T., GPS multipath mitigation in measurement domain and its application for high accuracy navigation, Proc. 17th International Technical Meeting of the Satellite Division of the Institute of Navigation, Long Beach, USA, 2004, pp. 1124–1130.
30. Kim, J.W., Jang, H.J., Hwang, D.-H., and Park, C., A step, stride, and heading determination for the pedestrian navigation system, Journal of Global Positioning Systems, 2004, vol. 3, no. 1–2, pp. 273–279.
31. Wang, L., Dong, Z., Pei, L., Qian, J., Liu, C., Liu, D., and Liu, P., A robust context-based heading estimation algorithm for pedestrian using a smartphone, Proc. 28th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION-GNSS+), Tampa, FL, USA, 2015, pp. 2493–2500.
32. Pei, L., Liu, D., Zou, D., Choy, R.L.F., Chen, Y., and He, Z., Optimal heading estimation based multidimensional particle filter for pedestrian indoor positioning, IEEE Access, 2018, vol. 6, pp. 49705– 49720, DOI: 10.1109/ACCESS.2018.2868792
33. Lan, H., Yu, C., El-Sheimy, N., An integrated PDR/GNSS pedestrian navigation system, China Satellite Navigation Conference (CSNC) 2015 Proceedings: Volume III. Lecture Notes in Electrical Engineering, vol 342, Berlin: Springer, 2015, pp. 677–690.
Review
For citations:
Rehman A., Shahid H., , Bhatti H. Accurate and Direct GNSS/PDR Integration Using Extended Kalman Filter for Pedestrian Smartphone Navigation. Giroskopiya i Navigatsiya. 2020;28(2):91-108. (In Russ.) https://doi.org/10.17285/0869-7035.0034
Views:
1
Email this article 