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Indoor Localization Fusing WiFi with Smartphone Inertial Sensors Using LSTM Networks

Research output: Contribution to journalArticlepeer-review

Mingyang Zhang, Jie Jia, Jian Chen, Yansha Deng, Xingwei Wang, Abdol Hamid Aghvami

Original languageEnglish
Article number9381997
Pages (from-to)13608-13623
Number of pages16
JournalIEEE Internet of Things Journal
Issue number17
Accepted/In press2021
Published1 Sep 2021

Bibliographical note

Funding Information: Manuscript received January 26, 2021; revised March 6, 2021; accepted March 11, 2021. Date of publication March 19, 2021; date of current version August 24, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 61772126, Grant 61972079, Grant 61872073, and Grant 62032013; in part by the National Key Research and Development Program of China under Grant 2018YFC0830601; in part by the Fundamental Research Funds for the Central Universities under Grant N2016004, Grant N2016002, and Grant N2024005-1; in part by the Central Government Guided Local Science and Technology Development Fund Project under Grant 2020ZY0003; in part by the LiaoNing Revitalization Talents Program under Grant XLYC1902010; in part by the Young and Middle-Aged Scientific and Technological Innovation Talent Support Program of Shenyang under Grant RC200548; and in part by the Joint Funds of Ministry of Education with China Mobile under Grant MCM20180203. (Corresponding author: Jie Jia.) Mingyang Zhang, Jian Chen, and Xingwei Wang are with the School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China (e-mail:;; Publisher Copyright: © 2014 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

King's Authors


Smartphone-based indoor localization has attracted considerable attentions in both research and industrial areas. However, the localization accuracy and robustness are still challenging problems due to low-cost noisy devices, especially in those complicated localization environments. Considering that pedestrian dead-reckoning (PDR) devices are widely equipped in recent smartphones, we propose a novel indoor localization fusing algorithm that integrates both wireless fidelity (WiFi) features and PDR features. By formulating the fusing indoor localization as a recursive function approximation problem, a sliding-window based displacement scheme is designed to generate a time-series based feature dataset. We further apply the long short-term memory (LSTM) network for data fusion and localization on this dataset by taking advantage of its benefits in time-series prediction and characterization. To evaluate the performance of the proposed algorithm, we compare it with state-of-the-art filter-based localization algorithms in three typical movements and three postures of holding smartphones. Extensive experiment results demonstrate the accuracy and robustness of the proposed algorithm in indoor localization, even in some extreme environments.

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