Abstract
Indoor positioning technology is commercially available now, however, the positioning accuracy is not sufficient in the current technologies. Currently available indoor positioning technologies differ in terms of accuracy, costs and effort, but have improved quickly in the last couple of years. It has been actively conducted research for estimating indoor location using RSSI (Received Signal Strength Indicator) level of Wi-Fi access points or BLE (Bluetooth Low Energy) tags. WiFi signal is commonly used for the indoor positioning technology. However, It requires an external power source, more setup costs and expensive. BLE is inexpensive, small, have a long battery life and do not require an external energy source. Therefore, using BLE tags we might be able to make the positioning system practical and inexpensive way. In this paper, we investigate such practical type of indoor positioning method based on BLE. BLE RSSI are processed by Multilayer Perceptron(MLP). Also, compass data and walking speed estimation with an extended Kalman filter is used to improve the accuracy. Our preliminary experimental result shows 2.21m error in case of the MLP output. In preliminary experimental results, the proposed approach improved the accuracy of indoor positioning by 21.2%.
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1. Villarubia, G., Rubio, F., De Paz, J. F., Bajo, J., & Zato, C. (2013). Applying classifiers in indoor location system. In Trends in Practical Applications of Agents and Multiagent Systems (pp. 53–58). Springer International Publishing.
2. Kaemarungsi, K., & Krishnamurthy, P. (2004, March). Modeling of indoor positioning systems based on location fingerprinting. In INFOCOM 2004. Twenty-third AnnualJoint Conference of the IEEE Computer and Communications Societies (Vol. 2, pp. 1012–1022). IEEE.
3. Liu, H., Darabi, H., Banerjee, P., & Liu, J. (2007). Survey of wireless indoor positioning techniques and systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 37(6), 1067–1080.
4. Sala, A. S. M., Quiros, R. G., & Lopez, E. E. (2010, June). Using neural networks and Active RFID for indoor location services. In Smart Objects: Systems, Technologies and Applications (RFID Sys Tech), 2010 European Workshop on (pp. 1–9). VDE.
5. Kumar, S., & Lee, S. R. (2014, June). Localization with RSSI values for wireless sensor networks: An artificial neural network approach. In International Electronic Conference on Sensors and Applications (Vol. 1). Multidisciplinary Digital Publishing Institute.
6. Zhang, R., Bannoura, A., Höflinger, F., Reindl, L. M., & Schindelhauer, C. (2013, February). Indoor localization using a smart phone. In Sensors Applications Symposium (SAS), 2013 IEEE (pp. 38–42). IEEE.
7. Liu, Y., Dashti, M., Rahman, M. A. A., & Zhang, J. (2014, March). Indoor localization using smartphone inertial sensors. In Positioning, Navigation and Communication (WPNC), 2014 11th Workshop on (pp. 1–6). IEEE.
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Kawai, T., Matsui, K., Honda, Y., Villarubia, G., Rodriguez, J.M.C. (2018). Preliminary study for improving accuracy on Indoor positioning method using compass and walking detect. In: Omatu, S., Rodríguez, S., Villarrubia, G., Faria, P., Sitek, P., Prieto, J. (eds) Distributed Computing and Artificial Intelligence, 14th International Conference. DCAI 2017. Advances in Intelligent Systems and Computing, vol 620. Springer, Cham. https://doi.org/10.1007/978-3-319-62410-5_39
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DOI: https://doi.org/10.1007/978-3-319-62410-5_39
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