Journal of Korea Society of Digital Industry and Information Management (디지털산업정보학회논문지)

Korea Society of Digital Industry and Information Management (디지털산업정보학회)
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Deep Learning-based Indoor Positioning System Using CSI

채널 상태 정보를 이용한 딥 러닝 기반 실내 위치 확인 시스템

  • 장중봉 (한양대학교 전자컴퓨터통신공학과) ;
  • 최승원 (한양대학교 전자전기공학부)
  • Received : 2020.08.26
  • Accepted : 2020.10.23
  • Published : 2020.12.30
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Abstract

Over the past few years, Wi-Fi signal based indoor positioning system (IPS) has been researched extensively because of its low expenses of infrastructure deployment. There are two major aspects of location-related information contained in Wi-Fi signals. One is channel state information (CSI), and one is received signal strength indicator (RSSI). Compared to the RSSI, the CSI has been widely utilized because it is able to reveal fine-grained information related to locations. However, the conventional IPS that employs a single access point (AP) does not exhibit decent performance especially in the environment of non-line-of-sight (NLOS) situations due to the reliability degeneration of signals caused by multipath fading effect. In order to address this problem, in this paper, we propose a novel method that utilizes multiple APs instead of a single AP to enhance the robustness of the IPS. In our proposed method, a hybrid neural network is applied to the CSIs collected from multiple APs. By relying more on the fingerprint constructed by the CSI collected from an AP that is less affected by the NLOS, we find that the performance of the IPS is significantly improved.

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References

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