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 - Funding Information:
Manuscript received April 2, 2020; revised September 4, 2020 and January 5, 2021; accepted January 14, 2021. Date of publication January 27, 2021; date of current version June 10, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 61371184, Grant 61671137, and Grant 61771114 and in part by the Application Foundation Projects of Science and Technology Department in Sichuan Province under Grant 2018JY0242 and Grant 2018JY0218. The associate editor coordinating the review of this article and approving it for publication was M. Ding. (Corresponding author: Xiansheng Guo.) Lin Li, Xiansheng Guo, Mengxue Zhao, and Huiyong Li are with the Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China (e-mail: linli9419@gmail.com; xsguo@uestc.edu.cn; zhaomengxue1027@163.com; hyli@uestc.edu.cn).
Publisher Copyright:
© 2002-2012 IEEE.
PY - 2021/6
Y1 - 2021/6
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 - Indoor localization
KW - heterogeneous feature space
KW - transfer learning
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U2 - 10.1109/TWC.2021.3052606
DO - 10.1109/TWC.2021.3052606
M3 - Article
AN - SCOPUS:85100485211
SN - 1536-1276
VL - 20
SP - 3628
EP - 3642
JO - IEEE Transactions on Wireless Communications
JF - IEEE Transactions on Wireless Communications
IS - 6
M1 - 9337208
ER -