Abstract
Wi-Fi fingerprint-based indoor localization has attracted much attention due to the pervasiveness of Wi-Fi access points (APs). However, the localization accuracy could be degraded due to signal fluctuations and noises of omnidirectional antennas. Some researches propose to leverage the signal distribution of different APs to enhance the localization accuracy. Nevertheless, they are either sensitive to signal errors or computationally costly.
In this paper, we propose a directional AP guided indoor localization system, where we incorporate directional APs to constrain the spatial space of clients. Based on the observation that signals of different APs fluctuate similarly, we study signal correlation between multiple APs. Consequently, we can identify anomalous APs (signal changes drastically compared with others) and filter them to reduce the adverse impact on the localization accuracy. Based on the correlation estimation, we model the localization problem with Dempster-Shafer (DS) theory and directional AP guidance to estimate the confidence values of AP signals adaptively. Furthermore, we remove the division in DS theory to avoid the paradox problem. We have implemented our algorithm and conducted extensive experiments in two different trial sites. Experimental results show that we can improve the localization accuracy by at least 27% compared with the state-of-the-art competing schemes.
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Notes
- 1.
We use “client” and “target” interchangeably in this paper.
References
Achroufene, A., Amirat, Y., Chibani, A.: RSS-based indoor localization using belief function theory. IEEE Trans. Autom. Sci. Eng. 16(3), 1163–1180 (2018)
Bigler, T., Treytl, A., Kienmayer, C.: Increasing localization robustness using directional antennas. In: 2017 22nd IEEE ETFA, pp. 1–4. IEEE (2017)
Chang, Y.J., Ou, C.H., Ssu, K.F.: A cluster analysis-based localization scheme for wireless sensor networks using mobile anchor nodes with directional antennas. In: 2018 IEEE ICASI, pp. 1156–1158. IEEE (2018)
Chen, Z., et al.: \({M}^3\): multipath assisted Wi-Fi localization with a single access point. IEEE Trans. Mobile Comput. 20(2), 588–602 (2021)
Choi, J., Lee, G., Choi, S., Bahk, S.: Smartphone based indoor path estimation and localization without human intervention. IEEE Trans. Mobile Comput. (2020). https://doi.org/10.1109/TMC.2020.3013113
He, S., Chan, S.H.G.: Tilejunction: mitigating signal noise for fingerprint-based indoor localization. IEEE Trans. Mobile Comput. 15(6), 1554–1568 (2015)
He, S., Chan, S.H.G.: INTRI: contour-based trilateration for indoor fingerprint-based localization. IEEE Trans. Mobile Comput. 16(6), 1676–1690 (2016)
Huang, B., Li, X., Mao, G., Jia, B., Li, W.: On the pedestrian flow analysis through passive WiFi sensing. In: 2019 IEEE GLOBECOM, pp. 1–6. IEEE (2019)
Jun, J., et al.: Low-overhead WiFi fingerprinting. IEEE Trans. Mobile Comput. 17(3), 590–603 (2017)
Laoudias, C., Moreira, A., Kim, S., Lee, S., Wirola, L., Fischione, C.: A survey of enabling technologies for network localization, tracking, and navigation. IEEE Commun. Surv. Tutor. 20(4), 3607–3644 (2018)
Li, L., Xie, P., Wang, J.: Enabling 3D ambient light positioning with mobile phones and battery-free chips. IEEE Trans. Mobile Comput. 20(3), 952–964 (2021)
Lohan, E.S., Talvitie, J., Granados, G.S.: Data fusion approaches for WiFi fingerprinting. In: 2016 IEEE ICL-GNSS, pp. 1–6. IEEE (2016)
Niu, Q., He, T., Liu, N., He, S., Luo, X., Zhou, F.: MAIL: multi-scale attention-guided indoor localization using geomagnetic sequences. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 4(2), 1–23 (2020)
Niu, Q., Li, M., He, S., Gao, C., Gary Chan, S.H., Luo, X.: Resource-efficient and automated image-based indoor localization. ACM Trans. Sen. Netw. 15(2), 1–31 (2019)
Pan, M.S., Li, K.Y.: ezNavi: an easy-to-operate indoor navigation system based on pedestrian dead reckoning and crowdsourced user trajectories. IEEE Trans. Mobile Comput. 20(2), 488–501 (2021)
Singh, M., Bhoi, S.K., Khilar, P.M.: Geometric constraint-based range-free localization scheme for wireless sensor networks. IEEE Sens. J. 17(16), 5350–5366 (2017)
Sun, Q., Ye, X., Gu, W.: A new combination rules of evidence theory. Acta Electron. Sin. 8, 117–119 (2000)
Zhang, M., Zhang, S., Cao, J.: Fusing received signal strength from multiple access points for WLAN user location estimation. In: 2008 IEEE ICICSE, pp. 173–180. IEEE (2008)
Zhu, X., Qu, W., Qiu, T., Zhao, L., Atiquzzaman, M., Wu, D.O.: Indoor intelligent fingerprint-based localization: principles, approaches and challenges. IEEE Commun. Surv. Tutor. 22(4), 2634–2657 (2020)
Acknowledgement
This work is supported by the National Natural Science Foundation of China (61972433), the Natural Science Foundation of Guangdong Province (2021A1515012242), and the Fundamental Research Funds for the Central Universities (19lgjc11).
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Zhu, K., Hu, Y., Liu, N., Niu, Q. (2021). Fusing Directional and Omnidirectional Wi-Fi Antennas for Accurate Indoor Localization. In: Liu, Z., Wu, F., Das, S.K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2021. Lecture Notes in Computer Science(), vol 12937. Springer, Cham. https://doi.org/10.1007/978-3-030-85928-2_7
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