A method of fingerprint indoor localization based on received signal strength difference by using compressive sensing

With the development of network technology, WLAN-based indoor localization plays an increasingly important role. Most current localization methods are based on the comparison between the received signal strength indication (RSSI) and the RSS in the database, whose nearest reference point is the loca...

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Bibliographic Details
Published in:EURASIP journal on wireless communications and networking 2020-04, Vol.2020 (1), p.1-13, Article 72
Main Authors: Yu, Xiao-min, Wang, Hui-qiang, Wu, Jin-qiu
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Chips (electronics)
Communications Engineering
Compressive sensing
Compressive strength
Engineering
Fingerprints
Heterogeneity
Indoor localization
Information Systems Applications (incl.Internet)
Local area networks
Localization
Machine Learning and Deep Learning Methods for Wireless Network Applications
Networks
Received signal strength difference
Signal strength
Signal,Image and Speech Processing
Smartphones
Wi-Fi
Wireless networks
WLAN
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Summary:With the development of network technology, WLAN-based indoor localization plays an increasingly important role. Most current localization methods are based on the comparison between the received signal strength indication (RSSI) and the RSS in the database, whose nearest reference point is the location point. However, since a uniform standard for measuring components of smartphones has not yet been established, the Wi-Fi chipsets on different smartphones may have different sensitivity levels to different Wi-Fi access points (APs) and channels. Even for the same signal, RSSI values obtained by different terminals at the same time and the same location may be different. Therefore, the impact of terminal heterogeneity on localization accuracy can be overlooked. To address this issue, a fusion method based on received signal strength difference and compressive sensing (RSSD-CS) is proposed in this paper, which can reduce the influence caused by the terminal heterogeneity. Besides, a fingerprint database is reconstructed from the existing reference point data. Experiments show that the proposed RSSD-CS algorithm can achieve high localization accuracy in indoor localization, and the accuracy is enhanced by 20.5% and 15.6% compared to SSD and CS algorithm.
ISSN:1687-1499
1687-1472
1687-1499
DOI:10.1186/s13638-020-01683-8