A Novel Adaptive Device-Free Passive Indoor Fingerprinting Localization Under Dynamic Environment
- 27 September 2024
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Network and Service Management
- Vol. 21 (6) , 6140-6152
Abstract
In recent years, indoor localization has attracted a lot of interest and has become one of the key topics of Internet of Things (IoT) research, presenting a wide range of application scenarios. With the advantages of ubiquitous universal Wi-Fi platforms and the "unconscious collaborative sensing" in the monitored target, Channel State Information (CSI)-based device-free passive indoor fingerprinting localization has become a popular research topic. However, most existing studies have encountered the difficult issues of high deployment labor costs and degradation of localization accuracy due to fingerprint variations in real-world dynamic environments. In this paper, we propose BSWCLoc, a device-free passive fingerprint localization scheme based on the beyond-sharing-weights approach. BSWCLoc uses the calibrated CSI phases, which are more sensitive to the target location, as localization features and performs feature processing from a two-dimensional perspective to ultimately obtain rich fingerprint information. This allows BSWLoc to achieve satisfactory accuracy with only one communication link, significantly reducing deployment consumption. In addition, a beyond-sharing-weights (BSW) method for domain adaptation is developed in BSWCLoc to address the problem of changing CSI in dynamic environments, which results in reduced localization performance. The BSW method proposes a dual-flow structure, where one flow runs in the source domain and the other in the target domain, with correlated but not shared weights in the adaptation layer. BSWCLoc greatly exceeds the state-of-the-art in terms of positioning accuracy and robustness, according to an extensive study in the dynamic indoor environment over 6 days.Keywords
Funding Information
- National Natural Science Foundation of China (62261028, 62261029, 62062040)
- Natural Science Foundation of Jiangxi Province (20242BAB20047, 20224ACB202007, 20212BAB212006)
- Outstanding Youth Project of Jiangxi Natural Science Foundation (20212ACB212003)
- Jiangxi Province Key Subject Academic and Technical Leader Funding Project (20212BCJ23017)
- Science and Technology Research Project of Jiangxi Provincial Department of Education (GJJ200333)
This publication has 40 references indexed in Scilit:
- PSOSVRPos: WiFi indoor positioning using SVR optimized by PSOExpert Systems with Applications, 2023
- Few-Shot Transfer Learning for Device-Free Fingerprinting Indoor Localization2022
- A survey on ubiquitous WiFi-based indoor localization system for smartphone users from implementation perspectivesCCF Transactions on Pervasive Computing and Interaction, 2022
- DFPhaseFL: a robust device-free passive fingerprinting wireless localization system using CSI phase informationNeural Computing and Applications, 2020
- Device-free passive wireless localization system with transfer deep learning methodJournal of Ambient Intelligence and Humanized Computing, 2020
- MSDFL: a robust minimal hardware low-cost device-free WLAN localization systemNeural Computing and Applications, 2019
- Device-Free Indoor Localization Using Wi-Fi Channel State Information for Internet of Things2018
- Fingerprint-Based Device-Free Localization in Changing Environments Using Enhanced Channel Selection and Logistic RegressionIEEE Access, 2017
- Taming the inconsistency of Wi-Fi fingerprints for device-free passive indoor localization2017
- Pilot: Passive Device-Free Indoor Localization Using Channel State Information2013