Research on Indoor Localization Method based on Deep Learning and Wireless Sensing

Yong Deng; Chuanhao Li

doi:10.54097/458z6w20

Authors

Yong Deng
Chuanhao Li

DOI:

https://doi.org/10.54097/458z6w20

Keywords:

Indoor Localization; Wireless Sensing; SAE; BiLSTM.

Abstract

Aiming at the problems of indoor localization accuracy due to scale difference and noise interference in wireless sensing, a deep learning indoor localization architecture for wireless sensing is proposed. Firstly, iBeacon devices are arranged in the localization area to collect location fingerprints, and then stacked autoencoder (SAE) is introduced to effectively capture the depth features of the data to improve the robustness of the subsequent localization model. To further enhance the global temporal sensing capability of the model, a bidirectional long short-term memory network (BiLSTM) is introduced for location prediction. The experimental results show that the algorithm achieves higher positioning accuracy and better generalization performance than the traditional indoor localization methods.

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References

Zhuang C S, Zhang D Y. A Robust WiFi Localization Algorithm Using Data Augmentation and Stacked Denoising Autoencoder [C]. 2023 35th Chinese Control and Decision Conference (CCDC) (2023): 1445-1450.

Yong L, Sun, Wei X, et al. Voronoi Diagram and Crowdsourcing-Based Radio Map Interpolation for GRNN Fingerprinting Localization Using WLAN[J]. Sensors (Basel, Switzerland), 2018.

Alqahtani A A S, Choudhury N. Machine Learning for Location Prediction Using RSSI On Wi-Fi 2.4 GHz Frequency Band [C], 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2021.

Aggarwal C C. Machine Learning with Shallow Neural Networks [M]. Neural Networks and Deep Learning: A Textbook. Cham; Springer International Publishing. 2018: 53-104.

Qiao F, Wu J, Li J, et al. Trustworthy Edge Storage Orchestration in Intelligent Transportation Systems Using Reinforcement Learning[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(7): 4443-56.

He S, Chan S H G. Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons[J].IEEE Communications Surveys & Tutorials, 2017, 18(1):466-490.

Jiao J, Li F, Deng Z L, et al. A Smartphone Camera-Based Indoor Positioning Algorithm of Crowded Scenarios with the Assistance of Deep CNN[J]. 2017, 17.

Das S, Tariq A, Santos T, et al. Recurrent Neural Networks (RNNs): Architectures, Training Tricks, and Introduction to Influential Research[M], Machine Learning for Brain Disorders, 2023: 117-38.

Sherstinsky A. Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network[J]. Physica D: Nonlinear Phenomena, 2020, 404: 132306.

Kingma D P, BA J J C. Adam: A Method for Stochastic Optimization[J]. 2014, abs/1412.6980.