Abstract
With the rapid development of human society, the scope and complexity of urban living space are increasing, and the demand for space position information is constantly improved by human activities. Especially in recent years, with the advent of the mobile Internet and the rapid popularization of smart phone terminals, the lifestyle and behavior habits of people are undergoing a huge change. Therefore, the positioning technology is also increasingly received by people. Among them, Wi-Fi fingerprint positioning is the most popular way. In this paper, we proposed Wi-Fi Attention Networks (WAN) for Wi-Fi fingerprint indoor positioning. In the WAN, a bidirectional LSTM is used to get a representation vector of Wi-Fi by summarizing the contextual information. Then, an attention mechanism is used to extract the Wi-Fi words which are important to the representation of Wi-Fi sequences and get a high-level representation vector. Finally, a fully connected network is used for classification. Experimental results demonstrate that WAN performs better than traditional machine learning methods on the publicly available dataset.
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Acknowledgement
This work is supported by the National Key R&D Program of China (No. 2018YFB1201500), the National Natural Science Foundation of China under (Grant No.61471055), and the Beijing Natural Science Foundation under Grant No. L171011, Beijing Major Science and Technology Special Projects under Grant No. Z181100003118012 and China Railway with project No. BX37.
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Zhang, T., Man, Y., Fang, X., Ren, L., Ma, Y. (2019). Wi-Fi Attention Network for Indoor Fingerprint Positioning. In: Tang, Y., Zu, Q., Rodríguez García, J. (eds) Human Centered Computing. HCC 2018. Lecture Notes in Computer Science(), vol 11354. Springer, Cham. https://doi.org/10.1007/978-3-030-15127-0_11
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DOI: https://doi.org/10.1007/978-3-030-15127-0_11
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