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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Practical and Accurate Indoor Localization System Using Deep Learning.

Jeonghyeon Yoon¹, Seungku Kim¹

¹Department of Electronics Engineering, Chungbuk National University, Cheongju 28644, Korea.

Sensors (Basel, Switzerland)

|September 23, 2022

Summary

This summary is machine-generated.

This study presents a practical smartphone-based indoor localization system using pedestrian dead reckoning (PDR) and deep learning. The proposed method achieves accurate indoor positioning with a distance error of 3-5 meters.

Keywords:

GPS deep learning indoor localization pedestrian dead reckoning

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Area of Science:

Computer Science
Electrical Engineering
Geomatics Engineering

Background:

Indoor localization is crucial for location-based services on smartphones.
Pedestrian dead reckoning (PDR) using inertial measurement units (IMUs) offers a practical indoor localization approach.
Existing deep learning methods for indoor localization can be complex and less practical.

Purpose of the Study:

To propose a smartphone-based indoor localization system utilizing PDR.
To develop a deep learning model for accurate moving speed estimation using accelerometer and GPS data.
To enhance positioning accuracy through advanced data processing and deep learning techniques.

Main Methods:

Smartphone-based pedestrian dead reckoning (PDR).
Deep learning model for moving speed estimation, using accelerometer and GPS data.
Data preprocessing, data augmentation, deep learning modeling, and heading direction correction.

Main Results:

Experimental validation in a 240m horseshoe-shaped indoor building.
Achieved a distance error of approximately 3 to 5 meters.
Demonstrated a practical and accurate indoor localization solution compared to conventional methods.

Conclusions:

The proposed smartphone-based PDR system with deep learning offers a practical and accurate indoor localization solution.
The integration of data preprocessing, augmentation, and heading correction significantly improves positioning accuracy.
This approach provides a viable alternative for location-based services in indoor environments.