Skip to main content
Log in

Low-Cost 3D Bluetooth Indoor Positioning with Least Square

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

This paper discusses low-cost 3D indoor positioning with Bluetooth smart device and least square methods. 3D indoor location has become more and more attractive and it hasn’t been well resolved. Almost each smart phone has a Bluetooth component and it can be used for indoor positioning and navigation in the nature of things. Least square algorithms are the powerful tools for linear and nonlinear parameters estimation. Various linear and nonlinear least square methods and their theoretical basics and application performance for indoor positioning have been studied. Simulation and hardware experiments results prove that nonlinear least square method is suitable for parameters estimation of Bluetooth signal propagation, and generalized least square method has better performance than total least square methods. Simulation and hardware experiments results also show that proposed method has the advantages of low cost, lost power consumption, perfect availability and high location accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Vahidnia, M. H. (2013). A hierarchical signal-space partitioning technique for indoor positioning with WLAN to support location-awareness in mobile map services. Wireless Personal Communications, 69, 689–719.

    Article  Google Scholar 

  2. Fang, S.-H. (2012). An enhanced ZigBee indoor positioning system with an ensemble approach. IEEE Communications Letters, 16(4), 564–567.

    Article  Google Scholar 

  3. Saad, S. S. (2011). A standalone RFID indoor positioning system using passive tags. IEEE Transactions on Industrial Electronics, 58(5), 1961–1970.

    Article  Google Scholar 

  4. Arias-de-Reyna, E. (2013). A cooperative localization algorithm for UWB indoor sensor networks. Wireless Personal Communications, 72, 85–99.

    Article  Google Scholar 

  5. Hazas, M. (2006). Broadband ultrasonic location systems for improved indoor positioning. IEEE Transactions on Mobile Computing, 5(5), 536–547.

    Article  Google Scholar 

  6. Yucel, H. (2012). Development of indoor positioning system with ultrasonic and infrared signals. In International symposium on innovations in intelligent systems and applications (INISTA) (pp. 1–4)

  7. Kim, Jongbae. (2008). Vision-based location positioning using augmented reality for indoor navigation. IEEE Transactions on Consumer Electronics, 54(3), 954–962.

    Article  Google Scholar 

  8. Blankenbach, J. (2012). A robust and precise 3D indoor positioning system for harsh environments. In Proceedings of the international conference on indoor positioning and indoor navigation (IPIN) (pp. 1–8).

  9. Cruz, O. (2011). 3D indoor location and navigation system based on bluetooth. In Proceedings of 21st international conference on electrical communications and computers (CONIELECOMP) (pp. 271–277).

  10. Liu, S. (2013). Face-to-face proximity estimation using bluetooth on smartphones. IEEE Transactions on Mobile Computing, 99, 1–14.

    Google Scholar 

  11. Zhang, L. (2013). A comprehensive study of bluetooth fingerprinting-based algorithms for localization. In Proceedings of 27th international conference on advanced information networking and applications workshops (WAINA) (pp. 300–305).

  12. Baniukevic, A. (2013). Hybrid indoor positioning with Wi-Fi and bluetooth architecture and performance. Proceedings of IEEE 14th International Conference on Mobile Data Management (MDM), 1, 207–216.

    Google Scholar 

  13. Wang, Yapeng. (2013). Bluetooth positioning using RSSI and triangulation methods. In Proceedings of IEEE Consumer Communications and Networking Conference (CCNC) (pp. 837–842).

  14. Zheng, Bo. (2010). An adaptive and stable method for fitting implicit polynomial curves and surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(3), 561–568.

    Article  Google Scholar 

  15. He, Z. (2013). Improved high resolution TOA estimation for OFDM-WLAN based indoor ranging. IEEE Wireless Communications Letters, 2(2), 163–166.

    Article  Google Scholar 

  16. Kay, S. (2013). A computationally efficient nonlinear least squares method using random basis functions. IEEE Signal Processing Letters, 20(7), 721–724.

    Article  Google Scholar 

  17. Sharp, I. (2013). Enhanced least-squares positioning algorithm for indoor positioning. IEEE Transactions on Mobile Computing, 12(8), 1640–1650.

    Article  Google Scholar 

  18. Ba, D. (2014). Convergence and stability of iteratively re-weighted least squares algorithms. IEEE Transactions on Signal Processing, 62(1), 183–195.

    Article  MathSciNet  Google Scholar 

  19. Qiu, N. (2010). Combining genetic algorithm and generalized least squares for geophysical potential field data optimized inversion. IEEE Geoscience and Remote Sensing Letters, 7(4), 660–664.

    Article  Google Scholar 

  20. Arablouei, R. (2014). Analysis of the gradient-descent total least-squares adaptive filtering algorithm. IEEE Transactions on Signal Processing, 62(5), 1256–1264.

    Article  MathSciNet  Google Scholar 

  21. Qu, C. (2013). Novel passive localization algorithm based on weighted restricted total least square. Journal of Systems Engineering and Electronics, 24(4), 592–599.

    Article  Google Scholar 

  22. Han, S. (1996). Extended generalized total least squares method for the identification of bilinear systems. IEEE Transactions on Signal Processing, 44(4), 1015–1018.

    Article  Google Scholar 

  23. Decuir, J. (2014). Introducing Bluetooth smart: Part 1: A look at both classic and new technologies. IEEE Consumer Electronics Magazine, 3(1), 12–18.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Honggui Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, H. Low-Cost 3D Bluetooth Indoor Positioning with Least Square. Wireless Pers Commun 78, 1331–1344 (2014). https://doi.org/10.1007/s11277-014-1820-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-014-1820-1

Keywords

Navigation