Device-free passive wireless localization system with transfer deep learning method
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ORIGINAL RESEARCH
Device‑free passive wireless localization system with transfer deep learning method Xinping Rao1 · Zhi Li1 · Yanbo Yang1 Received: 15 July 2019 / Accepted: 19 December 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Recently, device-free passive wireless indoor localization (DFPWL) has attracted great interest due to the widespread deployment of Wi-Fi devices and the rapid growth in demand for location-based services (LBS). The DFPWL fingerprinting approach based on channel state information (CSI) has become the mainstream method since its simple deployment and localization accuracy. It determines the location of the target from the new measurement CSI by collecting a training database that measures the CSI and using a machine learning classifier. However, we have found through experiments that even if the indoor environment does not change, the CSI fingerprint will be different from the CSI fingerprint in the database over time, and most of the CSI-based DFPWL fingerprinting method ignores this. To cope with the reduction in localization accuracy caused by the time-varying characteristic of CSI, we propose a novel transfer deep learning-based DFPWL system in this paper. It uses the CSI extracted from 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 APs and Monitor Devices. Unlike the other traditional CSI-based DFPWL fingerprinting approaches using the pre-processed CSI samples as fingerprints, our system utilizes the transfer deep learning (TDL) method to learn new feature representations from the CSI samples as fingerprints, which can simultaneously minimize the intra-class differences, maximize inter-class differences, and minimize the distribution differences between fingerprint database and test samples. Finally, the KNN algorithm is utilized to compare the test samples and the fingerprint database under the new feature representation to obtain the estimation location of the target. Experiment results show that our system can effectively improve localization accuracy compared to the other state-of-art, and can maintain stable localization accuracy for a long time without reacquiring the CSI fingerprint database. Keywords Device-free indoor localization · Channel state information (CSI) · Deep learning · Transfer learning
1 Introduction The rapidly growing demand for location-based services (LBS) has stimulated a lot of attention in accurate indoor localization over the years. Due to the widespread deployment of Wi-Fi devices, the Device-free Passive Wireless Localization (DFPWL) technology has become a research hotspot for indoor localization in recent years. It can locate the target that neither carries any equipment nor participates in the locating course by the influence of the target on the surrounding wireless signals (Zhou et al. 2015; Savazzi * Zhi Li [email protected] 1
Center for Complex Intelligent Networks, School of Mechano‑electronic Engineering
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