Advertisement

Wireless Personal Communications

, Volume 109, Issue 4, pp 2541–2560 | Cite as

Indoor Positioning Algorithm Fusing Multi-Source Information

  • Hengliang TangEmail author
  • Fei Xue
  • Tao Liu
  • Mingru Zhao
  • Chengang Dong
Article
  • 46 Downloads

Abstract

With the development of computer technology, mobile intelligent terminal and wireless local area network (WLAN), the applications of location services have shown significant growth, and much progress has been made both in the applications and researches. According to the actual application requirements, a robust indoor positioning algorithm fusing multi-source information was presented in this paper. Firstly, the methods based on the inertial navigation system (INS) and the received signal strength (RSS) of WLAN were discussed and together with their advantages and disadvantages. Then, in order to further improve the positioning performance, a fusion model based on the sparse signal representation theory was designed to integrate the INS and RSS information, and next the optimization solution approach for the fusion model was deeply discussed. Finally, the simulation experiments were designed and carried out, and the experimental results demonstrated the feasibility and effectiveness of the proposed fusion algorithm.

Keywords

Indoor positioning Multi-source information Fusion model Sparse representation RSS INS 

Notes

Acknowledgements

This paper is supported by the Beijing Key Laboratory of Intelligent Logistics System (No. BZ0211), Beijing Intelligent Logistics System Collaborative Innovation Center, Beijing youth top-notch talent plan of High-Creation Plan (No. 2017000026833ZK25), Canal Plan Leading Talent Project of Beijing Tongzhou District (No. YHLB2017038), General Program of Science and Technology Development Project of Beijing Municipal Education Commission of China (No. KM201710037001), Grass-roots Academic Team Building Project of Beijing Wuzi University (No. 2019XJJCTD04).

References

  1. 1.
    Leondes, C. T., & Yonezawa, K. (1978). Evaluation of geometric performance of global positioning system. IEEE Transactions on Aerospace and Electronic Systems, AES,14(3), 533–539.CrossRefGoogle Scholar
  2. 2.
    Parkinson, B. W., & Gilbert, S. W. (1983). NAVSTAR: Global positioning system—ten years later. Proceedings of the IEEE,71(10), 1177–1186.CrossRefGoogle Scholar
  3. 3.
    Dale, S. A., & Daly, P. (1987). The Soviet Union’s GLONASS navigation satellites. IEEE Aerospace and Electronic Systems Magazine,2(5), 13–17.CrossRefGoogle Scholar
  4. 4.
    Cinar, T., & Ince, F. (2005). Contribution of GALILEO to search and rescue. In International conference on recent advances in space technologies (pp. 254–259). Istanbul: Turkish Air Force Acad.Google Scholar
  5. 5.
    China Satellite Navigation Office. (2010). BeiDou navigation satellite system. In The 5th meeting of the United Nations international committee on global navigation satellite systems, Turin, Italy.Google Scholar
  6. 6.
    Sun, G., Chen, J., Guo, W., & Liu, K. R. (2005). Signal processing techniques in network-aided positioning: A survey of state-of-the-art positioning designs. IEEE Signal Processing Magazine,22(4), 12–23.CrossRefGoogle Scholar
  7. 7.
    Chon, M., & Cha, H. (2011). Life map: A smartphone-based context provider for location-based services. IEEE Pervasive Computing,10(2), 58–67.CrossRefGoogle Scholar
  8. 8.
    Liu, H., Darabi, H., Banerjee, P., & Liu, J. (2007). Survey of wireless indoor positioning techniques and systems. IEEE Transactions on Systems, Man, and Cybernetics,37(6), 1067–1080.CrossRefGoogle Scholar
  9. 9.
    Nilsson, J. O., Gupta, A. K., & Handel, P. (2014). Foot-mounted inertial navigation made easy. In International conference on indoor positioning and indoor navigation (pp. 24–29).Google Scholar
  10. 10.
    Sarkar, S., Ghose, A., & Misra, A. (2015). Improving the error drift of inertial navigation based indoor location tracking. In ACM 14th international conference on information processing in sensor networks (pp. 352–353).Google Scholar
  11. 11.
    Xu, Z., Wei, J., Zhang, B., & Yang, W. (2015). A robust method to detect zero velocity for improved 3D personal navigation using inertial sensors. Sensors,15(4), 7708–7727.CrossRefGoogle Scholar
  12. 12.
    Duong, P. D., & Suh, Y. S. (2015). Foot pose estimation using an inertial sensor unit and two distance sensors. Sensors,15(7), 15888–15902.CrossRefGoogle Scholar
  13. 13.
    Diaz, E. M. (2015). Inertial pocket navigation system: Unaided 3D positioning. Sensors,15(4), 9156–9178.CrossRefGoogle Scholar
  14. 14.
    Pham, D. D., & Suh, Y. S. (2016). Pedestrian navigation using foot-mounted inertial sensor and LIDAR. Sensors,16(1), 120.CrossRefGoogle Scholar
  15. 15.
    Ward, A., Jones, A., & Harper, A. (1997). A new location technique for the active office. IEEE Personal Communications,4(5), 42–47.CrossRefGoogle Scholar
  16. 16.
    Priyantha, N., Chakraborty, A., & Balakrishnan, H. (2000). The cricket location-support system. In ACM annual international conference on mobile computing and networking (MOBICOM) (pp. 32–43) Boston: ACM.Google Scholar
  17. 17.
    Want, R., Hopper, A., Falcao, A., & Gibbons, J. (1992). Active badge location system. ACM Transactions on Information Systems,10(1), 91–102.CrossRefGoogle Scholar
  18. 18.
    Gu, Y., Lo, A., & Niemegeers, I. (2009). A survey of indoor positioning systems for wireless personal networks. IEEE Transactions on Communications Surveys and Tutorials,11(1), 13–32.CrossRefGoogle Scholar
  19. 19.
    Jin, G. Y., Lu, X. Y., & Park, M. S. (2006). An indoor localization mechanism using active RFID tag. In IEEE international conference on sensor networks, ubiquitous and trustworthy computing (Vol. 1, pp. 40–43).Google Scholar
  20. 20.
    WhereNet real-time locating system. https://www.zebra.com/. Accessed 6 June 2017.
  21. 21.
    Ni, L. M., Liu, Y., Lan, Y. C., & Patil, A. P. (2004). LANDMARC: Indoor location sensing using active RFID. ACM Wireless Networks,10(6), 701–710.CrossRefGoogle Scholar
  22. 22.
    Zhang, Y., Liu, W., Fang, Y., & Wu, D. (2006). Secure localization and authentication in ultra-wideband sensor networks. IEEE Transactions on Selected Areas in Communications,24(41), 829–835.CrossRefGoogle Scholar
  23. 23.
    Jiménez, A. R., & Seco, F. (2017). Comparing Ubisense, BeSpoon, and DecaWave UWB location systems: Indoor performance analysis. IEEE Transactions on Instrumentation and Measurement,99, 1–12.Google Scholar
  24. 24.
    The Series 7000 Ubisense UWB sensors. http://www.ubisense.net/. Accessed 10 June 2017.
  25. 25.
    Positioning micro label. http://www.tsingoal.com/. Accessed 3 Sept 2017.
  26. 26.
    Huh, J. H., & Seo, K. (2017). An indoor location-based control system using Bluetooth beacons for IoT systems. Sensors,17(12), 2917.CrossRefGoogle Scholar
  27. 27.
    Bahl, P., & Padmanabhan, V. N. (2000). RADAR: An in-building RF-based user location and tracking system. In IEEE international conference on computer and communications (Vol. 2, pp. 775–784). Tel Aviv: IEEE.Google Scholar
  28. 28.
    EKAHAU. http://www.ekahau.com/. Accessed 7 Sept 2017.
  29. 29.
    Castro, P., Chiu, P., Kremenek, T., & Muntz, R. (2001). A probabilistic room location service for wireless networked environments. In International conference on ubiquitous computing (pp. 18–34). Seattle: ACM.Google Scholar
  30. 30.
    Youssef, M., & Agrawala, A. K. (2005). The Horus WLAN location determination system. In International conference on mobile systems, applications and services (pp. 205–218). Seattle: ACM.Google Scholar
  31. 31.
    Wu, C., Yang, Z., Liu, Y., & Xi, W. (2013). WILL: Wireless indoor localization without site survey. IEEE Transactions on Parallel and Distributed Systems,24(4), 839–848.CrossRefGoogle Scholar
  32. 32.
    Lim, H., Kung, L. C., Hou, J. C., & Luo, H. (2010). Zero-configuration indoor localization over IEEE 802.11 wireless infrastructure. Wireless Networks,16(2), 405–420.CrossRefGoogle Scholar
  33. 33.
    Chintalapudi, K., Iyer, A. P., & Padmanabhan, V. N. (2010). Indoor localization without the pain. ACM International Conference on Mobile Computing & Networking,49(1), 173–184.Google Scholar
  34. 34.
    Kaemarungsi, K. (2005). Efficient design of indoor positioning systems based on location fingerprinting. IEEE International Conference on Wireless Networks, Communications and Mobile Computing,1(6), 181–186.Google Scholar
  35. 35.
    Kaemarungsi, K., & Krishnamurthy, P. (2004). Modeling of indoor positioning system based on location fingerprinting. Proceedings of IEEE INFOCOM/Joint Conference of the IEEE Computer & Communications Societies,2(2), 1012–1022.Google Scholar
  36. 36.
    Donoho, D. (2006). Compressed sensing. IEEE Transactions on Information Theory,52(4), 1289–1306.MathSciNetzbMATHCrossRefGoogle Scholar
  37. 37.
    Li, S. T., & Wei, D. (2009). A survey on compressive sensing. Acta Automatica Sinica,35(11), 1369–1377.CrossRefGoogle Scholar
  38. 38.
    Shi, G. M., Liu, D. H., Gao, D. H., Liu, Z., Lin, J., & Wang, L. J. (2009). Advances in theory and application of compressed sensing. Acta Electronica Sinica,37(5), 1070–1081.Google Scholar
  39. 39.
    Wright, J., Ma, Y., Mairal, J., Sapiro, G., Huang, T. S., & Yan, S. (2010). Sparse representation for computer vision and pattern recognition. Proceedings of the IEEE,98(6), 1031–1044.CrossRefGoogle Scholar
  40. 40.
    Amaldi, E., & Kann, V. (1998). On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems. Theoretical Computer Science,209(1–2), 237–260.MathSciNetzbMATHCrossRefGoogle Scholar
  41. 41.
    Wright, J., Yang, A., Ganesh, A., Sastry, S., & Ma, Y. (2009). Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence,31(2), 210–227.CrossRefGoogle Scholar
  42. 42.
    Cai, S., Liao, W., Luo, C., Li, M., Huang, X., & Li, P. (2017). CRIL: An efficient online adaptive indoor localization system. IEEE Transactions on Vehicular Technology,66(5), 4148–4160.Google Scholar
  43. 43.
    Vasilyev, P., Pearson, S., Elgohary, M., Aboy, M., & Mcnames, J. (2017). Inertial and time-of-arrival ranging sensor fusion. Gait & Posture,54, 1–7.CrossRefGoogle Scholar
  44. 44.
    Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011). Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning,3(1), 1–122.zbMATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of InformationBeijing Wuzi UniversityBeijingChina

Personalised recommendations