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Construction of Hybrid Dual Radio Frequency RSSI (HDRF-RSSI) Fingerprint Database and Indoor Location Method

Sensors (Basel, Switzerland), 2020-05, Vol.20 (10), p.2981 [Peer Reviewed Journal]

2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2020 by the authors. 2020 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s20102981 ;PMID: 32456362

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  • Title:
    Construction of Hybrid Dual Radio Frequency RSSI (HDRF-RSSI) Fingerprint Database and Indoor Location Method
  • Author: Sun, Haotai ; Zhu, Xiaodong ; Liu, Yuanning ; Liu, Wentao
  • Subjects: Accuracy ; Algorithms ; deep learning ; fingerprint location ; Fingerprinting ; Fingerprints ; hybrid dual radio frequency fingerprint ; indoor and outdoor location ; Information industry ; Libraries ; Localization ; Location based services ; location based services (LBS) ; Machine learning ; Navigation ; Neural networks ; physical distance ; Product development ; Radio frequency ; Radio frequency identification ; Signal strength ; Social research ; Wireless access points ; Wireless networks
  • Is Part Of: Sensors (Basel, Switzerland), 2020-05, Vol.20 (10), p.2981
  • Description: Radio frequency communication technology has not only greatly improved public network service, but also developed a new technological route for indoor navigation service. However, there is a gap between the precision and accuracy of indoor navigation services provided by indoor navigation service and the expectation of the public. This study proposed a method for constructing a hybrid dual frequency received signal strength indicator (HDRF-RSSI) fingerprint library, which is different from the traditional RSSI fingerprint library constructing method in indoor space using 2.4G radio frequency (RF) under the same Wi-Fi infrastructure condition. The proposed method combined 2.4G RF and 5G RF on the same access point (AP) device to construct a HDRF-RSSI fingerprint library, thereby doubling the fingerprint dimension of each reference point (RP). Experimental results show that the feature discriminability of HDRF-RSSI fingerprinting is 18.1% higher than 2.4G RF RSSI fingerprinting. Moreover, the hybrid radio frequency fingerprinting model, training loss function, and location evaluation algorithm based on the machine learning method were designed, so as to avoid limitation that transmission point (TP) and AP must be visible in the positioning method. In order to verify the effect of the proposed HDRF-RSSI fingerprint library construction method and the location evaluation algorithm, dual RF RSSI fingerprint data was collected to construct a fingerprint library in the experimental scene, which was trained using the proposed method. Several comparative experiments were designed to compare the positioning performance indicators such as precision and accuracy. Experimental results demonstrate that compared with the existing machine learning method based on Wi-Fi 2.4G RF RSSI fingerprint, the machine learning method combining Wi-Fi 5G RF RSSI vector and the original 2.4G RF RSSI vector can effectively improve the precision and accuracy of indoor positioning of the smart phone.
  • Publisher: Switzerland: MDPI AG
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s20102981
    PMID: 32456362
  • Source: Geneva Foundation Free Medical Journals at publisher websites
    ROAD
    PubMed Central
    ProQuest Central
    DOAJ Directory of Open Access Journals
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