Fusion of WiFi, smartphone sensors and landmarks using the kalman filter for indoor localization

Sensors (Switzerland)

Volumn 15, Issue 1, 2015, Pages 715-732

  Chen, Zhenghua ^a   Zou, Han ^a,b   Jiang, Hao ^a,b   Zhu, Qingchang ^a   Soh, Yeng Chai ^a,b   Xie, Lihua ^a,b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Indoor localization; Kalman filter; Landmarks; PDR; WiFi

Indexed keywords

KALMAN FILTERS; SIGNAL ENCODING; SMARTPHONES; TELECOMMUNICATION SERVICES; WI-FI;

INDOOR LOCALIZATION; INDOOR LOCALIZATION AND NAVIGATIONS; LANDMARKS; LINEAR PERSPECTIVE; LOCALIZATION ACCURACY; OUTDOOR LOCALIZATIONS; PDR; PEDESTRIAN DEAD RECKONING (PDR);

LOCATION BASED SERVICES;

EID: 84920518240     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s150100715     Document Type: Article

References (32)

1. Martin, E.; Vinyals, O.; Friedland, G.; Bajcsy, R., Precise indoor localization using smart phones. In Proceedings of the International Conference on Multimedia (MM’10), Firenze, Italy, 25–29 October 2010; pp. 787–790.
2. Zou, H.; Jiang, H.; Lu, X.; Xie, L. An online sequential extreme learning machine approach to WiFi based indoor positioning. In Proceedings of 2014 IEEE World Forum on the Internet of Things (WF-IoT), Seoul, Korea, 6–8 March 2014; pp. 111–116.
3. Zou, H.; Wang, H.; Xie, L.; Jia, Q.S. An RFID indoor positioning system by using weighted path loss and extreme learning machine. In Proceedings of the 2013 IEEE 1st International Conference on Cyber-Physical Systems, Networks, and Applications (CPSNA), Taipei, Taiwan, 19–20 August 2013; pp. 66–71.
4. Zou, H.; Xie, L.; Jia, Q.S.; Wang, H. Platform and Algorithm Development for a RFID-Based Indoor Positioning System. Unmanned Syst. 2014, 2, 279–291.
5. Seybold, J.S. Introduction to RF Propagation; John Wiley & Sons: Hoboken, NJ, USA, 2005.
6. Jin, Y.; Toh, H.S.; Soh, W.S.; Wong, W.C. A robust dead-reckoning pedestrian tracking system with low cost sensors. In Proceedings of 2011 IEEE International Conference on the Pervasive Computing and Communications (PerCom), Seattle, WA, USA, 21–25 March 2011; pp. 222–230.
7. Alzantot, M.; Youssef, M. UPTIME: Ubiquitous pedestrian tracking using mobile phones. In Proceedings of 2012 IEEE theWireless Communications and Networking Conference (WCNC), Shanghai, China, 1–4 April 2012; pp. 3204–3209.
8. Evennou, F.; Marx, F. Advanced integration of WiFi and inertial navigation systems for indoor mobile positioning. Eurasip J. Appl. Signal Proc. 2006, 2006, 164–164.
9. Liu, J.; Chen, R.; Pei, L.; Guinness, R.; Kuusniemi, H. A Hybrid Smartphone Indoor Positioning Solution for Mobile LBS. Sensors 2012, 12, 17208–17233.
10. Liu, Y.; Wang, Q.; Liu, J.; Wark, T., MCMC-based indoor localization with a smart phone and sparse WiFi access points. In Proceedings of the 2012 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), Lugano, Switzerland, 19–23 March 2012; pp. 247–252.
11. Leppäkoski, H.; Collin, J.; Takala, J. Pedestrian navigation based on inertial sensors, indoor map, and WLAN signals. J. Signal Proc. Syst. 2013, 71, 287–296.
12. Wang, H.; Lenz, H.; Szabo, A.; Bamberger, J.; Hanebeck, U.D. WLAN-based pedestrian tracking using particle filters and low-cost MEMS sensors. In Proceedings of the 2007 4th Workshop on Positioning, Navigation and Communication (WPNC’07), Hannover, Germany, 22–22 March 2007; pp. 1–7.
13. Tarrío, P.; Besada, J.A.; Casar, J.R. Fusion of RSS and inertial measurements for calibration-free indoor pedestrian tracking. In Proceedings of the 2013 16th International Conference on Information Fusion (FUSION), Istanbul, Turkey, 9–12 July 2013; pp. 1458–1464.
14. Pei, L.; Guinness, R.; Chen, R.; Liu, J.; Kuusniemi, H.; Chen, Y.; Chen, L.; Kaistinen, J. Human behavior cognition using smartphone sensors. Sensors 2013, 13, 1402–1424.
15. Kwapisz, J.R.; Weiss, G.M.; Moore, S.A. Activity recognition using cell phone accelerometers. ACM SigKDD Explor. Newsl. 2011, 12, 74–82.
16. Wang, H.; Sen, S.; Elgohary, A.; Farid, M.; Youssef, M.; Choudhury, R.R. No Need to War-Drive: Unsupervised Indoor Localization. In Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services (MobiSys’12), LowWood Bay, Lake District, UK, 25–29 June 2012; pp. 197–210.
17. Ladetto, Q. On foot navigation: Continuous step calibration using both complementary recursive prediction and adaptive Kalman filtering. In Proceedings of the ION GPS, Salt Lake City, UT, USA, 19–22 September 2000; pp. 1735–1740.
18. Qi, H.; Moore, J.B. Direct Kalman filtering approach for GPS/INS integration. IEEE Trans. Aerosp. Electron. Syst. 2002, 38, 687–693.
19. Chu, H.J.; Tsai, G.J.; Chiang, K.W.; Duong, T.T. GPS/MEMS INS Data Fusion and Map Matching in Urban Areas. Sensors 2013, 13, 11280–11288.
20. Lim, C.H.; Wan, Y.; Ng, B.P.; See, C. A real-time indoor WiFi localization system utilizing smart antennas. IEEE Trans. Consum. Electron. 2007, 53, 618–622.
21. Kuban, D.A.; Dong, L.; Cheung, R.; Strom, E.; de Crevoisier, R. Ultrasound-based localization. Semin. Radiat. Oncol. 2005, 15, 180–191.
22. Gezici, S.; Tian, Z.; Giannakis, G.B.; Kobayashi, H.; Molisch, A.F.; Poor, H.V.; Sahinoglu, Z. Localization via ultra-wideband radios: A look at positioning aspects for future sensor networks. IEEE Signal Proc. Mag. 2005, 22, 70–84.
23. Waqar, W.; Chen, Y.; Vardy, A. Incorporating user motion information for indoor smartphone positioning in sparse Wi-Fi environments. In Proceedings of the 17th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems, Montreal, QC, Canada, 21–26 September 2014; pp. 267–274.
24. Teixeira, T.; Dublon, G.; Savvides, A. A survey of human-sensing: Methods for detecting presence, count, location, track, and identity. ACM Comput. Surv. 2010.
25. Groves, P.D., Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems; Artech House: Norwood, MA, USA, 2013.
26. Android Developers, 2014. Available online: http://developer.android.com/index.html (accessed on 3 October 2014).
27. Welch, G.; Bishop, G. An Introduction to the Kalman Filter; University of North Carolina at Chapel Hill: Chapel Hill, NC, USA, 1995.
28. Pande, A.; Zeng, Y.; Das, A.K.; Mohapatra, P.; Miyamoto, S.; Seto, E.; Henricson, E.K.; Han, J.J. Energy expenditure estimation with smartphone body sensors. In Proceedings of the 8th International Conference on Body Area Networks, Boston, MA, USA, 30 September–2 October 2013; pp. 8–14.
29. Arulampalam, M.S.; Maskell, S.; Gordon, N.; Clapp, T. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Proc. 2002, 50, 174–188.
30. Elliott, R.J.; Aggoun, L.; Moore, J.B. Hidden Markov Models; Springer: New York, NY, USA, 1994.
31. Li, F.; Zhao, C.; Ding, G.; Gong, J.; Liu, C.; Zhao, F. A reliable and accurate indoor localization method using phone inertial sensors. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing, Pittsburgh, PA, USA, 5–8 September 2012; pp. 421–430.
32. Nurminen, H. Position Estimation Using RSS Measurements with Unknown Measurement Model Parameters. Master’s Thesis, Tampere University of Technology, Tampere, Finland, December 2012.