Abstract
A Global Positioning System (GPS) is widely used as a method of outdoor localization. However, indoor localization system is required because the GPS signals are disturbed indoors. Although the fingerprinting method is a simple and inexpensive method of indoor localization, it is affected by environmental factors such as multipath effects. This paper proposes a Convolutional Neural Networks (CNN)-based localization model that consists of pooling layers for Wi-Fi fingerprinting indoor localization. These pooling layers of CNN can extract features of RSSI that contribute to the localization. Experimental results show that the proposed model reduces MAE about 16%. Furthermore, the coefficient of determination improves by 0.5 compared to the fingerprinting method.
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This work was supported by JSPS KAKENHI Grant Number 19K14045.
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Uehara, M., Kimura, M., Kobayashi, H. (2022). Varidation of Indoor Localization Method by CNN Using RSSI. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 235. Springer, Singapore. https://doi.org/10.1007/978-981-16-2377-6_84
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