Developing three dimensional localization system using deep learning and pre-trained architectures for IEEE 802.11 Wi-Fi

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.263185

Keywords:

3D Localization, Wi-Fi, Deep Learning classification technique, confusion matrix, IEEE 802.11

Abstract

The performance of Wi-Fi fingerprinting indoor localization systems (ILS) in indoor environments depends on the channel state information (CSI) that is usually restricted because of the fading effect of the multipath. Commonly referred to as the next positioning generation (NPG), the Wi-Fi™, IEEE 802.11az standard offers physical layer characteristics that allow positioning and enhanced ranging using conventional methods. Therefore, it is essential to create an indoor environment dataset of fingerprints of CIR based on 802.11az signals, and label all these fingerprints by their location data estimate STA locations based on a portion of the dataset for fingerprints. This work develops a model for training a convolutional neural network (CNN) for positioning and localization through generating IEEE® 802.11data. The study includes the use of a trained CNN to predict the position or location of several stations according to fingerprint data. This includes evaluating the performance of the CNN for multiple channel impulses responses (CIRs). Deep learning and Fingerprinting algorithms are employed in Wi-Fi positioning models to create a dataset through sampling the fingerprints channel at recognized positions in an environment. The model predicts the locations of a user according to a signal acknowledged of an unidentified position via a reference database. The work also discusses the influence of antenna array size and channel bandwidth on performance. It is shown that the increased training epochs and number of STAs improve the network performance. The results have been proven by a confusion matrix that summarizes and visualizes the undertaking classification technique. We use a limited dataset for simplicity and last in a short simulation time but a higher performance is achieved by training a larger data.

Supporting Agency

  • The authors acknowledge the University of Babylon, Iraq for their support and assistance.

Author Biographies

Aseel Hamoud Hamza, University of Babylon

College of Law

Sabreen Ali Hussein, University of Babylon

Department of Mathematics and Computer

College of Basic Education

Ghassan Ahmad Ismaeel, University of Mosul

Department of Clinical Laboratory Sciences

College of Pharmacy

Saad Qasim Abbas, Al-Turath University College

Department of Medical Instrument Engineering Technique

Musadak Maher Abdul Zahra, Al-Mustaqbal University College

Computer Techniques Engineering Department

Ahmad H. Sabry, Al-Nahrain University

Doctor of Control and Automation Engineering

Department of Computer Engineering

References

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Published

2022-08-31

How to Cite

Hamza, A. H., Hussein, S. A., Ismaeel, G. A., Abbas, S. Q., Zahra, M. M. A., & Sabry, A. H. (2022). Developing three dimensional localization system using deep learning and pre-trained architectures for IEEE 802.11 Wi-Fi. Eastern-European Journal of Enterprise Technologies, 4(9(118), 41–47. https://doi.org/10.15587/1729-4061.2022.263185

Issue

Vol. 4 No. 9(118) (2022): Information and controlling system

Section

Information and controlling system

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Copyright (c) 2022 Aseel Hamoud Hamza, Sabreen Ali Hussein, Ghassan Ahmad Ismaeel, Saad Qasim Abbas, Musadak Maher Abdul Zahra, Ahmad H. Sabry

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