TY - JOUR
T1 - TransLoc
T2 - A Heterogeneous Knowledge Transfer Framework for Fingerprint-Based Indoor Localization
AU - Li, Lin
AU - Guo, Xiansheng
AU - Zhao, Mengxue
AU - Li, Huiyong
AU - Ansari, Nirwan
N1 - Publisher Copyright:
IEEE
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021
Y1 - 2021
N2 - Transfer learning algorithms (TLAs) are often used to solve the distribution discrepancy issue in fingerprint-based indoor localization. However, existing TLAs cannot react well to real time changes in the environmental dynamics of the target space due to three remarkable shortcomings: a) redundant knowledge in source domain may lead to “negative transfer”; b) the required target domain samples to calculate the distributions are unrealistically feasible for real-time positioning; c) they cannot transfer knowledge efficiently across domains with heterogeneous feature spaces. In this paper, we propose TransLoc, a heterogeneous knowledge transfer framework for fingerprint-based indoor localization, which can perform knowledge transfer efficiently even with only one sample in the target domain. Specifically, we first refine the source domain according to the target domain by removing redundant knowledge in the source domain. Then, we derive a cross-domain mapping, which transfers the specific knowledge of one domain to another domain, to construct a homogeneous feature space. In this new feature space, the transfer weights are computed for training a classifier for target location prediction. To further train the framework efficiently, we combine the mapping and weights learning into a joint objective function and solve it by a three-step iterative optimization algorithm. Extensive simulation and real-world experimental results verify that TransLoc not only significantly outperforms state-of-the-art methods but is also very robust to changing environment.
AB - Transfer learning algorithms (TLAs) are often used to solve the distribution discrepancy issue in fingerprint-based indoor localization. However, existing TLAs cannot react well to real time changes in the environmental dynamics of the target space due to three remarkable shortcomings: a) redundant knowledge in source domain may lead to “negative transfer”; b) the required target domain samples to calculate the distributions are unrealistically feasible for real-time positioning; c) they cannot transfer knowledge efficiently across domains with heterogeneous feature spaces. In this paper, we propose TransLoc, a heterogeneous knowledge transfer framework for fingerprint-based indoor localization, which can perform knowledge transfer efficiently even with only one sample in the target domain. Specifically, we first refine the source domain according to the target domain by removing redundant knowledge in the source domain. Then, we derive a cross-domain mapping, which transfers the specific knowledge of one domain to another domain, to construct a homogeneous feature space. In this new feature space, the transfer weights are computed for training a classifier for target location prediction. To further train the framework efficiently, we combine the mapping and weights learning into a joint objective function and solve it by a three-step iterative optimization algorithm. Extensive simulation and real-world experimental results verify that TransLoc not only significantly outperforms state-of-the-art methods but is also very robust to changing environment.
KW - heterogeneous feature space
KW - Indoor localization
KW - transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85100485211&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85100485211&partnerID=8YFLogxK
U2 - 10.1109/TWC.2021.3052606
DO - 10.1109/TWC.2021.3052606
M3 - Article
AN - SCOPUS:85100485211
JO - IEEE Transactions on Wireless Communications
JF - IEEE Transactions on Wireless Communications
SN - 1536-1276
ER -