Next Article in Journal
Analysis of Cooperative Perception in Ant Traffic and Its Effects on Transportation System by Using a Congestion-Free Ant-Trail Model
Next Article in Special Issue
Design of a Millimeter-Wave Radar Remote Monitoring System for the Elderly Living Alone Using WIFI Communication
Previous Article in Journal
Ground Moving Target Imaging via SDAP-ISAR Processing: Review and New Trends
Previous Article in Special Issue
Indoor Positioning System Using Dynamic Model Estimation
 
 
Article

A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting

Institute of New Imaging Technologies, Jaume I University, 12071 Castelló de la Plana, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Yuh-Shyan Chen
Sensors 2021, 21(7), 2392; https://doi.org/10.3390/s21072392
Received: 18 February 2021 / Revised: 19 March 2021 / Accepted: 26 March 2021 / Published: 30 March 2021
Indoor localization is an enabling technology for pervasive and mobile computing applications. Although different technologies have been proposed for indoor localization, Wi-Fi fingerprinting is one of the most used techniques due to the pervasiveness of Wi-Fi technology. Most Wi-Fi fingerprinting localization methods presented in the literature are discriminative methods. We present a generative method for indoor localization based on Wi-Fi fingerprinting. The Received Signal Strength Indicator received from a Wireless Access Point is modeled by a hidden Markov model. Unlike other algorithms, the use of a hidden Markov model allows ours to take advantage of the temporal autocorrelation present in the Wi-Fi signal. The algorithm estimates the user’s location based on the hidden Markov model, which models the signal and the forward algorithm to determine the likelihood of a given time series of Received Signal Strength Indicators. The proposed method was compared with four other well-known Machine Learning algorithms through extensive experimentation with data collected in real scenarios. The proposed method obtained competitive results in most scenarios tested and was the best method in 17 of 60 experiments performed. View Full-Text
Keywords: hidden Markov models; indoor localization; machine learning; Wi-Fi fingerprinting hidden Markov models; indoor localization; machine learning; Wi-Fi fingerprinting
Show Figures

Figure 1

MDPI and ACS Style

Belmonte-Fernández, Ó.; Sansano-Sansano, E.; Caballer-Miedes, A.; Montoliu, R.; García-Vidal, R.; Gascó-Compte, A. A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting. Sensors 2021, 21, 2392. https://doi.org/10.3390/s21072392

AMA Style

Belmonte-Fernández Ó, Sansano-Sansano E, Caballer-Miedes A, Montoliu R, García-Vidal R, Gascó-Compte A. A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting. Sensors. 2021; 21(7):2392. https://doi.org/10.3390/s21072392

Chicago/Turabian Style

Belmonte-Fernández, Óscar, Emilio Sansano-Sansano, Antonio Caballer-Miedes, Raúl Montoliu, Rubén García-Vidal, and Arturo Gascó-Compte. 2021. "A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting" Sensors 21, no. 7: 2392. https://doi.org/10.3390/s21072392

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop