WiFi/PDR Integrated System for 3D Indoor Localization
In recent years, location-based services LBS have received extensive attention from scholars at home and abroad, and how to obtain location information is a very important issue. The creation of systems for solving problems of positioning and navigation inside buildings is a very perspective, actual and complicated task, especially in a multi-floor environment. To improve the indoor localization performance, we proposed a three-dimensional (3D) indoor localization system integrating WiFi/Pedestrian Dead Reckoning (PDR), where extended Kalman filter (EKF) is used to estimate target location. The algorithm first relies on MEMS in our mobile phones to evaluate the speed and heading angle of the test nodes. Second, for two-dimensional (2D) localization, the speed and heading angle as with as the results of the WiFi Fingerprint-based localization are utilized as the inputs to the EKF. Third, the proposed algorithm works out the height of the test nodes by utilize a barometer and geographical data which have been recorded in real time. Our experimental results in a real multi-layer environment indicate that the proposed WiFi/PDR integrated system algorithm means that the localization accuracy error is at least 1 m lower than WiFi and PDR itself.
KeywordsWi-Fi fingerprinting PDR Extended Kalman Filter Multi-floor positioning
This work is supported in part by Program for Changjiang Scholars and Innovative Research Team in University (IRT1299), Special Fund of Chongqing Key Laboratory (CSTC), Fundamental and Frontier Research Project of Chongqing (cstc2017jcyjAX0380), Scientific and Technological Research Foundation of Chongqing Municipal Education Commission (KJ1704083).
- 4.Tsukuda, D., Fujimoto, M., Oda, Y., Wada, T., Okada, H., Mutsuura, K.: A new indoor position estimation method of RFID tags for continuous moving navigation systems. In: IEEE International Conference on Indoor Positioning and Indoor Navigation, pp. 1–8 (2012)Google Scholar
- 5.Bahl, P., Padmanabhan, V.N.: RADAR: an in-building RF-based user location and tracking system. In: Annual Joint Conference of the IEEE Computer and Communications Societies, pp. 775–784 (2000)Google Scholar
- 7.Wu, C., Yang, Z., Liu, Y., et al.: WILL: wireless indoor localization without site survey. In: Proceedings of IEEE INFOCOM, pp. 64–72. IEEE Press (2012)Google Scholar
- 8.Yang, Z., Wu, C., Liu, Y.: Locating in fingerprint space: wireless indoor localization with little human intervention. In: International Conference on Mobile Computing and Networking, pp. 269–280. ACM (2012)Google Scholar
- 12.Malyavej, V., Kumkeaw, W., Aprpimai, M.: Indoor robot localization by RSSI/IMU sensor fusion. In: 10th IEEE International Conference on Computer, Telecommunications and Information Technology, Krabi, Thailand (2013)Google Scholar
- 13.Liu, X., Zhang, S., Lin, X.: WLAN/MARG integrated positioning system using data fusion. Syst. Eng. Electron. 34, 2361–2365 (2012)Google Scholar
- 14.Wang, H., Lenz, H., Szabo, A., Bamberger J., Hanebeck, U.D.: WLAN-based pedestrian tracking using particle filters and low-cost MEMS sensors. In: 4th IEEE Workshop on Positioning, Navigation and Communication (2007)Google Scholar
- 15.Atia, M.M., Korenberg, M., Noureldin, A.A.: WiFi-aided reduced inertial sensors-based navigation system with fast embedded implementation of particle filtering. In: IEEE International Symposium on Mechatronics and its Applications (2012)Google Scholar
- 17.Zhang, R., Bannoura, A., Hoflinger, F., Reindl, L.M., Schindelhauer, C.: Indoor Localization Using a Smart Phone. In: IEEE Sensors Applications Symposium (SAS), pp. 38–42 (2013)Google Scholar
- 19.Kim, B., Kong, S.H.: Indoor positioning based on Bayesian filter using magnetometer measurement difference. In: Vehicular Technology Conference, pp. 1–5. IEEE (2015)Google Scholar
- 22.Wei, L.: Localization Algorithm Analysis Based on K-means Clustering. J. Guangxi Univ. Technol. PP. 2696–2704 (2012)Google Scholar