期刊
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
卷 7, 期 1, 页码 316-327出版社
IEEE COMPUTER SOC
DOI: 10.1109/TNSE.2018.2871165
关键词
Indoor localization; fingerprinting; deep convolutional neural network; 5 GHz commodity Wi-Fi; Channel state information
资金
- NSF [CNS-1702957]
- Wireless Engineering Research and Education Center (WEREC) at Auburn University
With the increasing demand of location-based services, Wi-Fi based localization has attracted great interest because it provides ubiquitous access in indoor environments. In this paper, we propose CiFi, deep convolutional neural networks (DCNN) for indoor localization with commodity 5GHz WiFi. Leveraging a modified device driver, we extract phase data of channel state information (CSI), which is used to estimate the angle of arrival (AoA). We then create estimated AoA images as input to a DCNN, to train the weights in the offline phase. The location of mobile device is predicted based using the trained DCNN and new CSI AoA images. We implement the proposed CiFi system with commodity Wi-Fi devices in the 5GHz band and verify its performance with extensive experiments in two representative indoor environments.
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