Smartphone-Based Indoor Localization via Network Learning With Fusion of FTM/RSSI Measurements

This letter proposes a deep neural network (DNN)-based indoor localization approach that leverages WiFi Fine Timing Measurement (FTM) and Received Signal Strength Indicator (RSSI) as environment features to provide accurate location estimation. Our method uses DNN with raw FTM and RSSI measurements...

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Bibliographic Details
Published inIEEE networking letters Vol. 5; no. 1; pp. 21 - 25
Main Authors Eberechukwu, Paulson, Park, Hyunwoo, Laoudias, Christos, Horsmanheimo, Seppo, Kim, Sunwoo
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.03.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Artificial neural networks
Commercial off-the-shelf technology
Distance measurement
DNN
Fingerprint recognition
fingerprinting
FTM
Indoor environments
Indoor localization
Localization
Location awareness
Machine learning
measurement fusion
Measurement uncertainty
Noise measurement
RSSI
Signal strength
Smartphones
Training
Wireless fidelity
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Summary:This letter proposes a deep neural network (DNN)-based indoor localization approach that leverages WiFi Fine Timing Measurement (FTM) and Received Signal Strength Indicator (RSSI) as environment features to provide accurate location estimation. Our method uses DNN with raw FTM and RSSI measurements for self-learning and produces enhanced ranging information in the presence of measurement noise. Experimental data was obtained from real-world settings using commercial off-the-shelf devices in two different indoor office environments. The proposed solution was evaluated regarding the localization Mean Squared Error, demonstrating remarkable accuracy and outperforming state-of-the-art methods.
ISSN:2576-3156
2576-3156
DOI:10.1109/LNET.2022.3226462