A Fast Offline Database Construction Mechanism for Wi-Fi Fingerprint Based Localization Using Ultra-Wideband Technology
With the ever-increasing demand on location-based services (LBS), fingerprint-based methods have attracted more and more attention in indoor localization. However, the considerable overhead of fingerprint is still a problem which hinders the practicability of such technology. Due to the prevalent of Wi-Fi access points (APs) and the high location accuracy of Ultra-Wideband (UWB), in this paper, we propose a hybrid system which utilizes UWB and Wi-Fi technologies to alleviate the offline overhead and improve the localization accuracy. Specifically, we employ UWB to determine the coordinate of each reference point (RP) instead of traditional manual measurement. Meanwhile, the Received Signal Strength Indicator (RSSI) of Wi-Fi is collected by a customized software installed in the mobile device. Then, a timestamp matching scheme is proposed to fuse these data coming from different devices and construct the offline fingerprint database. Besides, in order to better map the online data with offline database, an AP weight assignment scheme is proposed, which allocates APs with different weights based on the RSSI characteristic in each RP. We implement the system in real-world environment and the experimental results demonstrate the effectiveness of the proposed method.
KeywordsIndoor localization Wi-Fi fingerprint UWB technology
This work was supported in part by the National Natural Science Foundation of China under Grant No. 61872049; the Frontier Interdisciplinary Research Funds for the Central Universities (Project No. 2018CDQYJSJ0034); and the Venture & Innovation Support Program for Chongqing Overseas Returnees (Project No. cx2018016).
- 8.Yang, Z., Wu, C., Liu, Y.: Locating in fingerprint space: wireless indoor localization with little human intervention. In: Proceedings of the 18th Annual International Conference on Mobile Computing and Networking, pp. 269–280. ACM, New York (2012)Google Scholar
- 11.Elbakly, R., Youssef, M.: A robust zero-calibration RF-based localization system for realistic environments. In: 2016 13th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), pp. 1–9. IEEE, London (2016)Google Scholar
- 12.Machaj, J., Brida, P., Piché, R.: Rank based fingerprinting algorithm for indoor positioning. In: 2011 International Conference on Indoor Positioning and Indoor Navigation, pp. 1–6. IEEE, Guimaraes (2011)Google Scholar
- 13.Han S., Zhao C., Meng W., Li, C.: Cosine similarity based fingerprinting algorithm in WLAN indoor positioning against device diversity. In: 2015 IEEE International Conference on Communications (ICC), pp. 2710–2714. IEEE, London (2015)Google Scholar
- 14.Silva, B., Pang, Z., Akerberg, J., Neander, J., Hancke, G.: Experimental study of UWB-based high precision localization for industrial applications. In: 2014 IEEE International Conference on Ultra-WideBand (ICUWB), pp. 280–285. IEEE, Paris (2014)Google Scholar
- 15.Martin, E., Vinyals, O., Friedland, G., Bajcsy, R.: Precise indoor localization using smart phones. In: Proceedings of the 18th ACM International Conference on Multimedia, pp. 787–790. ACM, New York (2010)Google Scholar
- 18.Shi, G., Ming, Y.: Survey of indoor positioning systems based on ultra-wideband (UWB) technology. In: Zeng, Q.-A. (ed.) Wireless Communications, Networking and Applications. LNEE, vol. 348, pp. 1269–1278. Springer, New Delhi (2016). https://doi.org/10.1007/978-81-322-2580-5_115CrossRefGoogle Scholar
- 20.Zhang, H., Liu, K., Jin, F., Feng, L., Lee, V., Ng, J.: A scalable indoor localization algorithm based on distance fitting and fingerprint mapping in Wi-Fi environments. Neural Comput. Appl. 2019, 1–15 (2019)Google Scholar
- 21.Zhang, H., et al.: An Annulus Local Search Based Localization (ALSL) algorithm in indoor Wi-Fi environments. In: 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced, pp. 887–892. IEEE, Guangzhou (2018)Google Scholar
- 23.Jin, F., Liu, K., Zhang, H., Wu, W., Cao, J., Zhai, X.: A zero site-survey overhead indoor tracking system using particle filter. In: ICC 2019–2019 IEEE International Conference on Communications (ICC), pp. 1–7. IEEE, Shanghai (2019)Google Scholar