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Wi-Fi Fine Time Measurement: Data Analysis and Processing for Indoor Localisation

Published online by Cambridge University Press:  04 May 2020

Yue Yu
Affiliation:
(State Key Laboratory of Information Engineering in Survey, Mapping and Remote Sensing, Wuhan University, 470002Wuhan, China)
Ruizhi Chen*
Affiliation:
(State Key Laboratory of Information Engineering in Survey, Mapping and Remote Sensing, Wuhan University, 470002Wuhan, China)
Zuoya Liu
Affiliation:
(State Key Laboratory of Information Engineering in Survey, Mapping and Remote Sensing, Wuhan University, 470002Wuhan, China)
Guangyi Guo
Affiliation:
(State Key Laboratory of Information Engineering in Survey, Mapping and Remote Sensing, Wuhan University, 470002Wuhan, China)
Feng Ye
Affiliation:
(State Key Laboratory of Information Engineering in Survey, Mapping and Remote Sensing, Wuhan University, 470002Wuhan, China)
Liang Chen
Affiliation:
(State Key Laboratory of Information Engineering in Survey, Mapping and Remote Sensing, Wuhan University, 470002Wuhan, China)
*

Abstract

Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. Wi-Fi based indoor localisation has become attractive due to its extensive distribution and low cost properties. IEEE 802.11-2016 now includes a Wi-Fi Fine Time Measurement (FTM) protocol which can be used for Wi-Fi ranging between intelligent terminal and Wi-Fi access point. This paper introduces a framework of Wi-Fi FTM data acquisition and processing that can be used for indoor localisation. We analyse the main factors that affect the accuracy of Wi-Fi ranging and propose a calibration, filtering and modelling algorithm that can effectively reduce the ranging error caused by clock deviation, non-line-of-sight (NLOS) and multipath propagation. Experimental results show that the proposed calibration and filtering method is able to achieve metre-level ranging accuracy in case of line-of-sight by using large bandwidth. Estimation results also show that the proposed Wi-Fi ranging model provides an accurate ranging performance in NLOS and multipath contained indoor environment; the final positioning error is less than 2·2 m with a stable output frequency of 3 Hz.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2020

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References

REFERENCES

Al-Jazzar, S., Caffery, J. and You, H. R. (2007). Scattering-model-based methods for TOA location in NLOS environments. IEEE Transactions on Vehicular Technology, 56(2), 583593.CrossRefGoogle Scholar
Alsindi, N. A., Alavi, B. and Pahlavan, K. (2008). Measurement and modeling of ultrawideband TOA-based ranging in indoor multipath environments. IEEE Transactions on Vehicular Technology, 58(3), 10461058.CrossRefGoogle Scholar
Banin, L., Schatzberg, U. and Amizur, Y. (2013). Next Generation Indoor Positioning System Based on WiFi Time of Flight. In Proceedings of 26th Int. Tech. Meeting Satellite Division Inst. Navigat.(ION GNSS+), pp. 975–982.Google Scholar
Banin, L., Schatzberg, U. and Amizur, Y. (2016). Wi-Fi FTM and Map Information Fusion for Accurate Positioning. In 2016 International Conference on Indoor Positioning and Indoor Navigation, Sapporo, Japan.Google Scholar
Bialer, O., Raphaeli, D. and Weiss, A. J. (2011). Efficient time of arrival estimation algorithm achieving maximum likelihood performance in dense multipath. IEEE Transactions on Signal Processing, 60(3), 12411252.CrossRefGoogle Scholar
Bisio, I., Cerruti, M., Lavagetto, F., Marchese, M., Pastorino, M., Randazzo, A., et al. (2014). A trainingless WiFi fingerprint positioning approach over mobile devices. IEEE Antennas & Wireless Propagation Letters, 13(1), 832835.CrossRefGoogle Scholar
Burgess, S., Kuang, Y. and Astrom, K. (2013). TOA sensor network calibration for receiver and transmitter spaces with difference in dimension. Signal Processing, 107(C), 3342.CrossRefGoogle Scholar
Chan, Y. T., Tsui, W. Y. and So, H. C. (2006). Time-of-arrival based localization under NLOS conditions. IEEE Transactions on Vehicular Technology, 55(1), 1724.CrossRefGoogle Scholar
Chen, C., Chen, Y. and Han, Y. (2016). Achieving centimeter-accuracy indoor localization on WiFi platforms: A multi-antenna approach. IEEE Internet of Things Journal, 4(1), 122134.Google Scholar
Chintalapudi, K., Iyer, A. P. and Padmanabhan, V. N. (2010). Indoor Localization Without the Pain. Sixteenth International Conference on Mobile Computing and Networking. ACM, Chicago, USA.CrossRefGoogle Scholar
Chuang, S. F., Wu, W. R. and Liu, Y. T. (2015). High-resolution AOA estimation for hybrid antenna arrays. IEEE Transactions on Antennas & Propagation, 63(7), 29552968.CrossRefGoogle Scholar
Dvorecki, N., Bar-Shalom, O., Banin, L. and Amizur, Y. (2019). A Machine Learning Approach for Wi-Fi RTT Ranging. Proceedings of the 2019 International Technical Meeting of The Institute of Navigation, January 28–31, Hyatt Regency Reston, Reston, Virginia.CrossRefGoogle Scholar
Gao, S., Zhang, F. and Wang, G. (2017). NLOS error mitigation for TOA-based source localization with unknown transmission time. IEEE Sensors Journal, PP(99), 1–1.Google Scholar
Hamilton, B. R., Ma, X., Zhao, Q. and Xu, J. (2008). ACES: Adaptive Clock Estimation and Synchronization Using Kalman Filtering. International Conference on Mobile Computing and Networking, MOBICOM 2008, San Francisco, California, USA.CrossRefGoogle Scholar
Hanssens, B., Tanghe, E. and Gaillot, D. P., Liénard, M., Oestges, C., Plets, D., Martens, L. and Joseph, W. (2018). An extension of the RiMAX multipath estimation algorithm for ultra-wideband channel modeling. EURASIP Journal on Wireless Communications & Networking, 2018(1), 164.CrossRefGoogle ScholarPubMed
He, J., Geng, Y., Liu, F. and Xu, C. (2014). CC-KF: Enhanced TOA performance in multipath and NLOS indoor extreme environment. Sensors Journal IEEE, 14(11), 37663774.Google Scholar
He, J., Pahlavan, K., Li, S. and Wang, Q. (2013). A testbed for evaluation of the effects of multipath on performance of TOA-based indoor geolocation. IEEE Transactions on Instrumentation & Measurement, 62(8), 22372247.CrossRefGoogle Scholar
He, S. and Chan, S. H. G. (2015). Wi-Fi fingerprint-based indoor positioning: Recent advances and comparisons. IEEE Communications Surveys & Tutorials, 18(1), 466490.CrossRefGoogle Scholar
He, Z., Ma, Y. and Tafazolli, R. (2013). Improved High Resolution TOA Estimation for OFDM-WLAN Based Indoor Ranging. IEEE Wireless Communications Letters, 2(2), 163166.CrossRefGoogle Scholar
Ibrahim, M., Liu, H., Minitha Jawahar, V. N., et al. (2018). Verification: Accuracy Evaluation of Wi-Fi Fine Time Measurements on an Open Platform. Proceedings of the 24th Annual International Conference on Mobile Computing and Networking, New Delhi, India.CrossRefGoogle Scholar
IEEE Std 802.11 (2016) IEEE Standard for Information Technology – Telecommunications and Information Exchange between Systems. Local and Metropolitan Area Networks – Specific Requirements – Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications. (Revision of IEEE Std 802.11-2012). pages 1–3534, December 2016.Google Scholar
Li, X. and Pahlavan, K. (2004). Super-resolution TOA estimation with diversity for indoor geolocation. IEEE Transactions on Wireless Communications, 3(1), 224234.CrossRefGoogle Scholar
Liu, H., Darabi, H., Banerjee, P. and Liu, J. (2007). Survey of wireless indoor positioning techniques and systems. IEEE Transactions on Systems Man & Cybernetics Part C, 37(6), 10671080.CrossRefGoogle Scholar
McCrady, D. D., Doyle, L. Forstrom, H., Dempsey, T. and Martorana, M. (2000). Mobile ranging using low-accuracy clocks. IEEE Transactions on Microwave Theory and Techniques, 48(6), 951958.CrossRefGoogle Scholar
Mrstik, A. V. and Smith, P. G. (1978). Multipath limitations on low-angle radar tracking. IEEE Transactions on Aerospace and Electronic Systems, 14(1), 85102.CrossRefGoogle Scholar
Navarro, M. and Najar, M. (2011). Frequency domain joint TOA and DOA estimation in IR-UWB. IEEE Transactions on Wireless Communications, 10(10), 111.CrossRefGoogle Scholar
Niesen, U., Ekambaram, V. N., Jose, J. and Wu, X. (2017). Intervehicle Range Estimation from Periodic Broadcasts. 66, pp. 10637–10646.CrossRefGoogle Scholar
Paziewski, J. and Wielgosz, P. (2014). Assessment of GPS + Galileo and multi-frequency Galileo single-epoch precise positioning with network corrections. Gps Solutions, 18(4), 571579.CrossRefGoogle Scholar
Rea, M., Fakhreddine, A. and Giustiniano, D. (2017). Filtering Noisy 802.11 Time-of-Flight Ranging Measurements from Commoditized WiFi Radios. IEEE/ACM Transactions on Networking, 2017, 114.Google Scholar
Saito, K., Takada, J. I. and Kim, M. (2016). Characteristics Evaluation of Dense Multipath Component in 11GHz-Band Indoor Environment. 2016 10th European Conference on Antennas and Propagation (EuCAP), IEEE, 2016.CrossRefGoogle Scholar
Schatzberg, U., Banin, L. and Amizur, Y. (2014). Enhanced WiFi ToF indoor Positioning System with MEMS-Based INS and Pedometric Information. In Proceedings of 2014 IEEE/ION Position, Location and Navigation Symposium-PLANS 2014, pp. 185–192.CrossRefGoogle Scholar
Schmidt, R. (1986). Multiple emitter location and signal parameter estimation. IEEE Transactions on Antennas & Propagation, 34(3), 276280.CrossRefGoogle Scholar
Sharp, I. and Yu, K. (2014). Indoor TOA error measurement, modeling, and analysis. IEEE Transactions on Instrumentation & Measurement, 63(9), 21292144.CrossRefGoogle Scholar
Shen, J., Molisch A, F. and Salmi, J. (2012). Accurate passive location estimation using TOA measurements. IEEE Transactions on Wireless Communications, 11(6), 21822192.CrossRefGoogle Scholar
Wang, G., Chen, H., Li, Y. and Ansari, N. (2014). NLOS error mitigation for TOA-based localization via convex relaxation. IEEE Transactions on Wireless Communications, 13(8), 41194131.CrossRefGoogle Scholar
Wi-Fi Certified Location. (2017). https://google/BSUCdG. Accessed 9 September 2018.Google Scholar
Wu, K., Xiao, J., Yi, Y., Chen, D., Luo, X. and Ni, L. M. (2013). CSI-based indoor localization. IEEE Transactions on Parallel & Distributed Systems, 24(7), 13001309.CrossRefGoogle Scholar
Xiong, J., Sundaresan, K. and Jamieson, K. (2015). ToneTrack: Leveraging Frequency-Agile Radios for Time-Based Indoor Wireless Localization. MobiCom 2015. Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, Association for Computing Machinery, pp. 537–549.CrossRefGoogle Scholar
Xiong, W., Liu, C., Hu, S. and Li, S. (2015). High resolution TOA estimation based on compressed sensing. Wireless Personal Communications, 84(4), 114.CrossRefGoogle Scholar
Yang, Z., Zhou, Z. and Liu, Y. (2013). From RSSI to CSI: Indoor localization via channel response. ACM Computing Surveys (CSUR), 46(2), 132.CrossRefGoogle Scholar
Yi, L., Razul, S. G., Lin, Z. and Chong, M. S. (2013). Target tracking in mixed LOS/NLOS environments based on individual measurement estimation and LOS detection. IEEE Transactions on Wireless Communications, 13(1), 99111.CrossRefGoogle Scholar
Yin, F., Fritsche, C., Gustafsson, F. and Zoubir, A. M. (2013). TOA-based robust wireless geolocation and Cramér-Rao lower bound analysis in harsh LOS/NLOS environments. IEEE Transactions on Signal Processing, 61(9), 22432255.CrossRefGoogle Scholar
Zhang, D., Liu, Y., Guo, X., Gao, M. and Ni, L. M. (2012). On Distinguishing the Multiple Radio Paths in RSS-Based Ranging. IEEE INFOCOM. IEEE, Orlando, USA.Google Scholar
Zhang, J., Salmi, J. and Lohan, E. S. (2013). Analysis of kurtosis-based LOS/NLOS identification using indoor MIMO channel measurement. IEEE Transactions on Vehicular Technology, 62(6), 28712874.CrossRefGoogle Scholar
Zhang, S., Gao, S., Wang, G. and Li, Y. (2015). Robust NLOS error mitigation method for TOA-based localization via second-order cone relaxation. Communications Letters IEEE, 19(12), 22102213.CrossRefGoogle Scholar
Zhuang, Y., Lan, H., Li, Y. and Elsheimy, N. (2015). PDR/INS/Wifi integration based on handheld devices for indoor pedestrian navigation. Micromachines, 6(6), 793812.CrossRefGoogle Scholar