[time]
2020-09-10T13:49:31+02:00

[team_name]
SZU

[QMUL]


[UCL]


[team_institution]
Shenzhen University (SZU), Queen Mary University of London (QMUL) and University College London (UCL)

[logolink]


[system_name]
Bang Wu

[website]


[track]
3

[reference_person]
Bang Wu

[email]
bang.wu@qmul.ac.uk

[description]
The main principle of our approach contains six parts: 
WiFi Fingerprint positioning, Pedestrian Dead Reckoning (PDR) positioning, Mobility Detection, Floor Level Detection, Indoor/Outdoor Detection and Data Fusion.

The details of our method referring https://drive.google.com/file/d/1yy5M-sEtk-P1_GtRwLUf9CiSgMvxjDtm/view?usp=sharing
(if the link is unavailable, please feel free to contact me.)

[references]
[1]	B. Wu, Z. Ma, S. Poslad, and W. Zhang, “An efficient wireless access point selection algorithm for location determination based on RSSI interval overlap degree determination,” in Wireless Telecommunications Symposium (WTS), 2018, 2018, pp. 1–8.
[2]	Zhang, W., Hua, X., Yu, K., Qiu, W., Chang, X., Wu, B. and Chen, X., 2017. Radius based domain clustering for WiFi indoor positioning. Sensor Review, 37(1), pp.54-60.
[3]	Z. Ma, S. Poslad, J. Bigham, X. Zhang, and L. Men, “A BLE RSSI ranking based indoor positioning system for generic smartphones,” in Wireless Telecommunications Symposium (WTS), 2017, 2017, pp. 1–8.
[4]	Z. Ma, S. Poslad, S. Hu, and X. Zhang, “A fast path matching algorithm for indoor positioning systems using magnetic field measurements,” 28th IEEE Ann. Int. Symp. Personal, Indoor, and Mobile Radio Communications (PIMRC), 2017, pp. 1–5.
[5]	F. Li, C. Zhao, G. Ding, J. Gong, C. Liu, and F. Zhao, “A reliable and accurate indoor localization method using phone inertial sensors,” in Proceedings of the 2012 ACM Conference on Ubiquitous Computing, 2012, pp. 421–430.
[6]	C. Arcidiacono, S. M. Porto, M. Mancino, and G. Cascone, “A threshold-based algorithm for the development of inertial sensor-based systems to perform real-time cow step counting in free-stall barns,” Biosystems Engineering, vol. 153, pp. 99–109, 2017.
[7]	T. Oshin, S. Poslad, “ERSP: An Energy-efficient Real-time Smartphone Pedometer,” Int. Conf. on Systems, Man, and Cybernetics (SMC), 2013, pp. 2067 – 2072
[8]	Oshin T O, Poslad S, Zhang Z. Energy-efficient real-time human mobility state classification using smartphones[J]. IEEE Transactions on Computers, 2015, 64(6): 1680-1693.