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Real-time indoor localization using smartphone magnetic with LSTM networks

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Abstract

Due to the pervasiveness of geomagnetic fields and independence from external infrastructure, localization with smartphone-based magnetic has attracted considerable attention. However, existing approaches are still facing the problem of localization accuracy because of their low discernibility in geomagnetic features. Due to the need to traverse the magnetic database, these approaches cannot obtain localization results in real-time, especially in large indoor localization areas. To address these problems, a novel magnetic indoor localization approach based on long short-term memory networks (LSTMs) is proposed. The magnetic indoor localization is first formulated as a recursive function approximation problem. Based on the analysis of magnetic characteristics, a double sliding window-based dimension expansion approach is designed to generate a time-series magnetic feature dataset. The LSTMs are invoked for magnetic 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 implement the proposed LSTMs-based magnetic localization approach on a real Android-based smartphone and compare it with fingerprint-based localization algorithms. Extensive experiment results demonstrate the accuracy, response time, and robustness of the proposed algorithm in indoor localization.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 61772126, and 61972079, in part by the National Key Research and Development Program of China under Grants 2018YFC0830601, in part by the Fundamental Research Funds for the Central Universities under Grants N2016004, N2016002, and N2024005-1, in part by the joint Funds of Ministry of Education with China Mobile under Grant MCM20180203, in part by the Central Government Guided Local Science and Technology Development Fund Project under Grant 2020ZY0003, and in part by the Young and Middle-aged Scientific and Technological Innovation Talent Support Program of Shenyang under Grant RC200548.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China

    Mingyang Zhang, Jie Jia, Jian Chen, Leyou Yang, Liang Guo & Xingwei Wang

  2. Engineering Research Center of Security Technology of Complex Network System, Ministry of Education, Shenyang, 110819, China

    Jie Jia

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  1. Mingyang Zhang
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Correspondence to Jie Jia.

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Zhang, M., Jia, J., Chen, J. et al. Real-time indoor localization using smartphone magnetic with LSTM networks. Neural Comput & Applic 33, 10093–10110 (2021). https://doi.org/10.1007/s00521-021-05774-5

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