DFPhaseFL: a robust device-free passive fingerprinting wireless localization system using CSI phase information
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ORIGINAL ARTICLE
DFPhaseFL: a robust device-free passive fingerprinting wireless localization system using CSI phase information Xinping Rao1 • Zhi Li1 • Yanbo Yang1 • Shengyang Wang1 Received: 25 June 2019 / Accepted: 7 March 2020 Ó Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Device-free passive wireless indoor localization is attracting great interest in recent years due to the widespread deployment of Wi-Fi devices and the numerous location-based services requirements. In this paper, we propose DFPhaseFL, the first device-free fingerprinting indoor localization system that purely uses CSI phase information. It utilizes the CSI phase information extracted from simply a single link to estimate the location of the target, neither requiring the target to wear any electronic equipment nor deploying a large number of access points and monitor devices. In DFPhaseFL, the raw CSI phases are extracted from the CSI measurements through the three antennas of the Intel WiFi Link 5300 wireless Network Interface Card (IWL 5300 NIC) firstly. Then, linear transformation and noise filtering are applied to acquire the calibrated CSI phases. Through experimental observations, we find that the calibrated CSI phase owns an unpredictable characteristic over time. Thus, it cannot be directly applied as a fingerprint. To this end, a transfer deep supervised neural network method combining deep neural network and transfer learning is proposed to obtain feature representations with both transferability and discriminability as fingerprints. Then, the DFPhaseFL system uses the SVM algorithm to obtain the estimation of the target location online. Experiment results demonstrate that the DFPhaseFL owns a better estimation precision compared with the other state of art, and maintain a stable localization accuracy for a long time without reacquiring the fingerprint database. Keywords Device-free indoor localization Channel state information (CSI) Phase calibration Transfer deep learning.
1 Instruction Over the past decade, the location-based services (LBS) requirements have stimulated a lot of attention in accurate indoor localization. In recent years, due to the widespread deployment of Wi-Fi devices, the device-free passive wireless localization (DFPWL) technology has become a research hotspot in the field of indoor localization [1, 2]. It uses the influence of the target on surrounding wireless signals to locate the target that neither equips any device nor actively participates in the process of locating. There are much researches that use the easily acquired received signal strength to achieve device-free indoor localization & Zhi Li [email protected] 1
Center for Complex Intelligent Networks, School of Mechano-Electronic Engineering, Xidian University, Xi’an 710071, China
[3]. However, due to its own defects, the RSS is seriously affected by multipath effects in indoor environments. The deployment cost, detection granularity, and localization accuracy of the s
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