Earticle

초록

영어

Real Time Location System (RTLS) refers to a system that provides various services by measuring location information of objects in real time. The RTLS system is being used in many fields related to the Internet of Things (IoT), such as medical, healthcare, performances, and production facilities. A high-accuracy positioning system is essential for quality of RTLS. A variety of methods such as triangulation, trilateration, and MDS are utilized for positioning, but each has its own drawbacks. We propose an efficient and accurate advanced positioning system with Deep Reinforcement Learning (DRL). In the learning environment, we adopt the Proximal Policy Optimization (PPO) algorithm and Adam Optimizer. The proposed system estimates the exact position with a small amount of computation using only distance information from four anchor nodes in a 3D environment. Through system performance evaluation, we proved that the proposed system showed superior performance compared to the existing system.

목차

Abstract
I. INTRODUCTION
II. SYSTEM DESIGN
A. System Overview
B. Ranging Operation
C. Positioning based Deep Reinforcement Learning
III. PERFORMANCE EVALUATION
A. Simulation Implementation
B. Model Learning Result
C. Accuracy Comparison with Trilateration
IV. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Sangmin Lee [ Department of Electrical Engineering Korea University ]
• Hwangnam Kim [ Department of Electrical Engineering Korea University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004