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Using phase shift fingerprints and inertial measurements in support of precise localization in urban areas

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Abstract

Localization is an important primitive that is utilized in a number of important applications such as location-based mobile services, augmented reality, and autonomous mobile robotics. While the GPS technology is considered the de facto standard for outdoor localization, it is known to suffer from significant accuracy limitation in urban areas. In this work, we present a particle filter–based data fusion technique for localization in urban areas. The proposed localization technique provides more accurate location estimation results due to its ability to efficiently fuse together information collected from diverse sensor technologies. The novelty of our proposed approach stems from its ability to fuse data from diverse sources, namely, phase shift fingerprints collected from Low Power AM Radio (LPAM) towers and inertial measurement sensors. Our simulation results indicate that the proposed approach can achieve an accuracy of 0.5 m using a limited number of LPAM towers as low as 5. Also, the proposed approach requires the collection of a low number of LPAM phase shift fingerprints. Our simulations indicate that 30 fingerprints are enough to provide 0.5 m accuracy in a 100 × 100 m2 deployment.

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References

  1. De San Bernabe Clemente A, Martínez-de Dios JR, Baturone AO (2012) A WSN-based tool for urban and industrial fire-fighting. Sensors 12(11):15009–15035

    Article  Google Scholar 

  2. Lilien LT, Elbes MW, Othmane LB, Salih RM (2013) Simulation of emergency response operations for a static chemical spill within a building using an opportunistic resource utilization network. In: 2013 IEEE international conference on technologies for homeland security (HST). IEEE, pp 408–413

  3. Daniels RC, Heath RW (2012) Link adaptation with position/motion information in vehicle-to-vehicle networks. IEEE Trans Wirel Commun 11(2):505–509

    Article  Google Scholar 

  4. Dammann A, Agapiou G, Bastos J, Brunelk L, Garcia M, Guillet J, Ma Y, Ma J, Nielsen JJ, Ping L, Raulefs R, Rodriguez J, Slock D, Yang D, Yi N (2013) Where2 location aided communications. In: European wireless 2013; 19th European wireless conference, pp 1–8

  5. Qudah I, Leijdekkers P, Gay V (2010) Using mobile phones to improve medication compliance and awareness for cardiac patients. In: Proceedings of the 3rd international conference on pervasive technologies related to assistive environments, PETRA’10. ACM, New York, pp 36:1–36:7

  6. Ye H, Gu T, Tao X, Lu J. (2014) F-loc: floor localization via crowdsourcing. In: 2014 20th IEEE international conference on parallel and distributed systems (ICPADS), pp 47–54

  7. Chen PW, Ou KS, Chen KS (2010) IR indoor localization and wireless transmission for motion control in smart building applications based on wiimote technology. In: Proceedings of SICE annual conference, 2010, pp 1781–1785

  8. Karray F, Jmal MW, Abid M, Obeid AM, BenSaleh MS (2017) Demo: Earnardui–a wireless sensor network testbed for water pipeline monitoring. In: Proceedings of the 2017 international conference on embedded wireless systems and networks, EWSN’2017, USA, Junction Publishing, pp 244–245

  9. Ciurana M, Francisco B, Cugno S (2006) Indoor tracking in wlan location with toa measurements. In: Proceedings of the 4th ACM international workshop on mobility management and wireless access, MobiWac’06. ACM, New York, pp 121–125

  10. Cheng L, Wu C, Zhang Y (2011) Indoor robot localization based on wireless sensor networks. IEEE Trans Consum Electron 57:1099–1104

    Article  Google Scholar 

  11. Grewal MS, Andrews AP (2014) Kalman filtering: theory and practice with MATLAB, 4th. Wiley, Hoboken

    MATH  Google Scholar 

  12. Brown RG, Hwang PYC (1997) Introduction to random signals and applied Kalman filtering: with MATLAB exercises and solutions, 3rd. Wiley, New York

    MATH  Google Scholar 

  13. Julier SJ, Uhlmann JK (2004) Unscented filtering and nonlinear estimation. In: Proceedings of the IEEE. publisher:, pp 401–422

  14. Yang Z, Zhou Z, Liu Y (2013) From RSSI CSI: indoor localization via channel response. ACM Comput Surv (CSUR) 46(2):25

    Article  Google Scholar 

  15. He S, Chan S-HG (2016) Wi-fi fingerprint-based indoor positioning: recent advances and comparisons. IEEE Commun Surv Tutorials 18(1):466–490

    Article  Google Scholar 

  16. Han S, Lim H, Lee J (2007) An efficient localization scheme for a differential-driving mobile robot based on RFID system. IEEE Trans Ind Electron 54(6):3362–3369

    Article  Google Scholar 

  17. Park S, Hashimoto S (2009) Autonomous mobile robot navigation using passive RFID in indoor environment. IEEE Trans Ind Electron 56(7):2366–2373

    Article  Google Scholar 

  18. Zhou C, Griffin JD (2012) Accurate phase-based ranging measurements for backscatter rfid tags. IEEE Antennas Wirel Propag Lett 11:152–155

    Article  Google Scholar 

  19. Park S, Lee H (2013) Self-recognition of vehicle position using UHF Passive RFID tags. IEEE Trans Ind Electron 60(1):226– 234

    Article  Google Scholar 

  20. Sarkka S, Viikari VV, Huusko M, Jaakkola K (2012) Phase-based UHF RFID tracking with nonlinear Kalman filtering and smoothing. IEEE Sensors J 12(5):904–910

    Article  Google Scholar 

  21. Priyantha NB, Chakraborty A, Balakrishnan H (2000) The cricket location-support system. In: Proceedings of the 6th annual international conference on mobile computing and networking, MobiCom’00. ACM, New York, pp 32–43

  22. Thrun S, Burgard W, Fox D (2005) Probabilistic robotics. MIT Press, Cambridge

    MATH  Google Scholar 

  23. Ko NY, Kim TG (2012) Comparison of kalman filter and particle filter used for localization of an underwater vehicle. In: 2012 9th international conference on ubiquitous robots and ambient intelligence (URAI), pp 350–352

  24. Xiao Y, Ou Y, Feng W (2017) Localization of indoor robot based on particle filter with ekf proposal distribution. In: 2017 IEEE international conference on cybernetics and intelligent systems (CIS) and IEEE conference on robotics, automation and mechatronics (RAM), pp 568–571

  25. Knobloch D (2017) Practical challenges of particle filter based uwb localization in vehicular environments. In: 2017 International conference on indoor positioning and indoor navigation (IPIN), pp 1–5

  26. Lipka M, Sippel E, Hehn M, Adametz J, Vossiek M, Dobrev Y, Gulden P (2018) Wireless 3d localization concept for industrial automation based on a bearings only extended kalman filter. In: 2018 Asia-pacific microwave conference (APMC), pp 821–823

  27. Elbes M, Al-Fuqaha A, Anan M (2013) A precise indoor localization approach based on particle filter and dynamic exclusion techniques. Network Protocols and Algorithms 5(2):50–71

    Article  Google Scholar 

  28. Luo RC, Chen O (2009) Indoor human dynamic localization and tracking based on sensory data fusion techniques. In: International conference on intelligent robots and systems. IEEE, pp 860–865

  29. Laviola JJ (2003) A comparison of unscented and extended Kalman filtering for estimating quaternion motion. In: Proceedings of the 2003 American control conference, 2003, vol 3. IEEE, pp 2435–2440

  30. Chao C-H, Chu C-Y, Wu A-Y (2008) Location-constrained particle filter human positioning and tracking system, pp 73–76

  31. Belakbir A, Amghar M, Sbiti N (2014) Sensor data fusion for an indoor and outdoor localization. Radioelectron Commun Syst 57(4):149–158

    Article  Google Scholar 

  32. Kok M, Wahlström N, Schön TB, Gustafsson F (2013) MEMS-based inertial navigation based on a magnetic field map, pp 6466–6470

  33. Walter CSS, Silva YMLR, de Lucena VF Jr (2017) A location technique based on hybrid data fusion used to increase the indoor location accuracy. Procedia Computer Science 113:368

    Article  Google Scholar 

  34. Emter T, Saltoglu A, Petereit J (2010) Multi-sensor fusion for localization of a mobile robot in outdoor environments. In: 41St international symposium on robotics (ISR) 2010 and 6th german conference on robotics (ROBOTIK) 2010. VDE, pp 1–6

  35. Al-Fuqaha A, Elbes M, Rayes A (2013) An intelligent data fusion technique based on the particle filter to perform precise outdoor localization. International Journal of Pervasive Computing and Communications 9 (2):163–183. https://doi.org/10.1108/IJPCC-02-2013-0001

    Article  Google Scholar 

  36. Wu K, Xiao J, Yi Y, Chen D, Luo X, Ni LM (2013) CSI-Based indoor localization. IEEE Trans Parallel Distrib Syst 24(7):1300–1309

    Article  Google Scholar 

  37. Halperin D, Hu W, Sheth A, Wetherall D (2010) Predictable 802.11 packet delivery from wireless channel measurements. SIGCOMM Comput Commun Rev 41(4):159–170

    Article  Google Scholar 

  38. Martinelli F (2015) A robot localization system combining RSSI and phase shift in UHF-RFID signals. IEEE Trans Control Syst Technol 23(5):1782–1796

    Article  Google Scholar 

  39. Zhang Y, Chen X, Wang H, Chen L (2015) Accurate indoor localization with channel state information. In: Proceedings of the 3rd international workshop on energy harvesting; energy neutral sensing systems, ENSsys’15. ACM, New York, pp 35–36

  40. Youssef M, Agrawala A (2005) The Horus WLAN location determination system. In: Proceedings of the 3rd International Conference on Mobile Systems, Applications, and Services, MobiSys’05. ACM, New York, pp 205–218

  41. Wang X, Gao L, Mao S, Pandey S (2017) CSI-based fingerprinting for indoor localization: a deep learning approach. IEEE Trans Veh Technol 66(1):763–776

    Google Scholar 

  42. Ballou G (2015) Handbook for sound engineers. Taylor and Francis Group, Routledge

    Book  Google Scholar 

  43. Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1(1):33–57

    Article  Google Scholar 

  44. Elbes M, Alzubi S, Kanan T, Al-Fuqaha A, Hawashin B (2019) A survey on particle swarm optimization with emphasis on engineering and network applications, Evolutionary Intelligence, pp 1–17

  45. Viel B, Asplund M (2014) Why is fingerprint-based indoor localization still so hard?. In: 2014 IEEE international conference on pervasive computing and communications workshops (PERCOM Workshops). IEEE, pp 443–448

  46. Langowski A (2008) Carrier and sampling frequency offset estimation and tracking in ofdm systems, pp 319–323

  47. Yousefi S, Narui H, Dayal S, Ermon S, Valaee S (2017) A survey on behavior recognition using WiFi channel state information. IEEE Commun Mag 55(10):98–104

    Article  Google Scholar 

  48. Sen S, Radunovic B, Choudhury RR, Minka T (2012) You are facing the Mona Lisa: spot localization using PHY layer information. In: Proceedings of the 10th international conference on mobile systems, applications, and services, MobiSys’12. ACM, New York, pp 183–196

  49. Priwgharm R, Chemtanomwong P (2011) A comparative study on indoor localization based on rssi measurement in wireless sensor network. In: 2011 eighth international joint conference on computer science and software engineering (JCSSE), pp 1–6

  50. Elbes M, Al-Fuqaha A, Rayes A (2012) Gyroscope drift correction based on TDoA technology in support of pedestrian dead reckoning, pp 314–319

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Authors and Affiliations

  1. Department of Computer Science, Alzaytoonah University of Jordan, Amman, Jordan

    Mohammed Elbes

  2. Department of Computer and Information Systems, Alzaytoonah University of Jordan, Amman, Jordan

    Ahmad Alkhatib

  3. Information and Computing Technology (ICT) Division, College of Science and Engineering (CSE), Hamad Bin Khalifa University, Doha, Qatar

    Ala Al-Fuqaha

  4. Department of Computer Science, Western Michigan University, Kalamazoo, MI, 49008, USA

    Ala Al-Fuqaha

  5. Department of Electrical Engineering, Information Technology University (ITU), Lahore, Pakistan

    Junaid Qadir

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  1. Mohammed Elbes
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  2. Ahmad Alkhatib
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  3. Ala Al-Fuqaha
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  4. Junaid Qadir
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Correspondence to Mohammed Elbes.

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Elbes, M., Alkhatib, A., Al-Fuqaha, A. et al. Using phase shift fingerprints and inertial measurements in support of precise localization in urban areas. Pers Ubiquit Comput 23, 861–872 (2019). https://doi.org/10.1007/s00779-019-01227-y

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  • DOI: https://doi.org/10.1007/s00779-019-01227-y

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