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.
Similar content being viewed by others
References
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.
Fang, S.-H. (2012). An enhanced ZigBee indoor positioning system with an ensemble approach. IEEE Communications Letters, 16(4), 564–567.
Saad, S. S. (2011). A standalone RFID indoor positioning system using passive tags. IEEE Transactions on Industrial Electronics, 58(5), 1961–1970.
Arias-de-Reyna, E. (2013). A cooperative localization algorithm for UWB indoor sensor networks. Wireless Personal Communications, 72, 85–99.
Hazas, M. (2006). Broadband ultrasonic location systems for improved indoor positioning. IEEE Transactions on Mobile Computing, 5(5), 536–547.
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)
Kim, Jongbae. (2008). Vision-based location positioning using augmented reality for indoor navigation. IEEE Transactions on Consumer Electronics, 54(3), 954–962.
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).
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).
Liu, S. (2013). Face-to-face proximity estimation using bluetooth on smartphones. IEEE Transactions on Mobile Computing, 99, 1–14.
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).
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.
Wang, Yapeng. (2013). Bluetooth positioning using RSSI and triangulation methods. In Proceedings of IEEE Consumer Communications and Networking Conference (CCNC) (pp. 837–842).
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.
He, Z. (2013). Improved high resolution TOA estimation for OFDM-WLAN based indoor ranging. IEEE Wireless Communications Letters, 2(2), 163–166.
Kay, S. (2013). A computationally efficient nonlinear least squares method using random basis functions. IEEE Signal Processing Letters, 20(7), 721–724.
Sharp, I. (2013). Enhanced least-squares positioning algorithm for indoor positioning. IEEE Transactions on Mobile Computing, 12(8), 1640–1650.
Ba, D. (2014). Convergence and stability of iteratively re-weighted least squares algorithms. IEEE Transactions on Signal Processing, 62(1), 183–195.
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.
Arablouei, R. (2014). Analysis of the gradient-descent total least-squares adaptive filtering algorithm. IEEE Transactions on Signal Processing, 62(5), 1256–1264.
Qu, C. (2013). Novel passive localization algorithm based on weighted restricted total least square. Journal of Systems Engineering and Electronics, 24(4), 592–599.
Han, S. (1996). Extended generalized total least squares method for the identification of bilinear systems. IEEE Transactions on Signal Processing, 44(4), 1015–1018.
Decuir, J. (2014). Introducing Bluetooth smart: Part 1: A look at both classic and new technologies. IEEE Consumer Electronics Magazine, 3(1), 12–18.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
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
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-014-1820-1