*Article* **Practical and Accurate Indoor Localization System Using Deep Learning**

**Jeonghyeon Yoon and Seungku Kim \***

> Department of Electronics Engineering, Chungbuk National University, Cheongju 28644, Korea

**\*** Correspondence: kimsk@cbnu.ac.kr

**Abstract:** Indoor localization is an important technology for providing various location-based services to smartphones. Among the various indoor localization technologies, pedestrian dead reckoning using inertial measurement units is a simple and highly practical solution for indoor localization. In this study, we propose a smartphone-based indoor localization system using pedestrian dead reckoning. To create a deep learning model for estimating the moving speed, accelerometer data and GPS values were used as input data and data labels, respectively. This is a practical solution compared with conventional indoor localization mechanisms using deep learning. We improved the positioning accuracy via data preprocessing, data augmentation, deep learning modeling, and correction of heading direction. In a horseshoe-shaped indoor building of 240 m in length, the experimental results show a distance error of approximately 3 to 5 m.

**Keywords:** indoor localization; pedestrian dead reckoning; deep learning; GPS
