TY - JOUR
T1 - DarLoc
T2 - Deep learning and data-feature augmentation based robust magnetic indoor localization
AU - Wang, Qinghu
AU - Jia, Jie
AU - Deng, Yansha
AU - Chen, Jian
AU - Wang, Xingwei
AU - Huang, Min
AU - Aghvami, Abdol Hamid
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2024/6/15
Y1 - 2024/6/15
N2 - Magnetic-based indoor localization has attracted considerable attention due to the pervasiveness of geomagnetic fields and is free of extra infrastructures. However, existing approaches still face heterogeneity problems caused by the type of devices, the holding attitudes, and the moving speeds of pedestrians. To cope with it, we propose a novel deep learning and data-feature augmentation based magnetic localization framework (DarLoc). First, we invoke orientation-insensitive magnetic signal extraction to remove the Direct Current (DC) component of the sequence and to eliminate the impact caused by different holding attitudes and various mobile devices. Second, we propose novel data augmentation and feature augmentation methods to extract features of speed information, thus tackling the multi-scale sequence problem caused by different moving speeds. Finally, we propose a deep multi-scale spatial–temporal learning model to extract both spatial and temporal features of the augmented sequences and to provide a robust localization for people with various moving speeds and attitudes. Extensive experiments, including a total of 189 volunteers with 4 different mobile devices and multiple moving speeds, are employed to evaluate the performance of DarLoc over 14 months. Experiment results reveal that: (1) DarLoc obtains an average localization accuracy of 0.47 m and 0.56 m under constant and variable moving speeds scenarios, respectively; (2) DarLoc obtains about 41% and 60% localization accuracy improvement compared with the state-of-the-art localization methods.
AB - Magnetic-based indoor localization has attracted considerable attention due to the pervasiveness of geomagnetic fields and is free of extra infrastructures. However, existing approaches still face heterogeneity problems caused by the type of devices, the holding attitudes, and the moving speeds of pedestrians. To cope with it, we propose a novel deep learning and data-feature augmentation based magnetic localization framework (DarLoc). First, we invoke orientation-insensitive magnetic signal extraction to remove the Direct Current (DC) component of the sequence and to eliminate the impact caused by different holding attitudes and various mobile devices. Second, we propose novel data augmentation and feature augmentation methods to extract features of speed information, thus tackling the multi-scale sequence problem caused by different moving speeds. Finally, we propose a deep multi-scale spatial–temporal learning model to extract both spatial and temporal features of the augmented sequences and to provide a robust localization for people with various moving speeds and attitudes. Extensive experiments, including a total of 189 volunteers with 4 different mobile devices and multiple moving speeds, are employed to evaluate the performance of DarLoc over 14 months. Experiment results reveal that: (1) DarLoc obtains an average localization accuracy of 0.47 m and 0.56 m under constant and variable moving speeds scenarios, respectively; (2) DarLoc obtains about 41% and 60% localization accuracy improvement compared with the state-of-the-art localization methods.
KW - Data and feature augmentation
KW - Deep learning
KW - Indoor localization
KW - Multi-scale features
UR - http://www.scopus.com/inward/record.url?scp=85180550612&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2023.122921
DO - 10.1016/j.eswa.2023.122921
M3 - Article
AN - SCOPUS:85180550612
SN - 0957-4174
VL - 244
JO - Expert Systems with Applications
JF - Expert Systems with Applications
M1 - 122921
ER -