Indoor Positioning by Double Deep Q-Network in VLC-Based Empty Office Environment

Recently, artificial intelligence (AI) has been applied in various industries. One such application is indoor user positioning using Big Data. The traditional method for positioning is the global positioning system (GPS). However, the performance of GPS is limited indoors due to propagation loss. He...

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Bibliographic Details
Published in:Applied sciences 2024-05, Vol.14 (9), p.3684
Main Authors: Oh, Sung Hyun, Kim, Jeong Gon
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
artificial intelligence (AI)
Big data
deep reinforcement learning (DRL)
Global Positioning System
Global positioning systems
GPS
indoor positioning
Light emitting diodes
Neural networks
Optimization
Processing speed
Questions and answers
received signal strength (RSS)
Receivers & amplifiers
Simulation
Transmitters
visible light communication (VLC)
Wi-Fi
Wireless communications
Work environment
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Summary:Recently, artificial intelligence (AI) has been applied in various industries. One such application is indoor user positioning using Big Data. The traditional method for positioning is the global positioning system (GPS). However, the performance of GPS is limited indoors due to propagation loss. Hence, radio frequency (RF)-based communication methods such as WiFi and Bluetooth have been proposed as indoor positioning solutions. However, positioning performance inaccuracies arise due to signal interference caused by RF band saturation. Therefore, this study proposes indoor user positioning based on visible light communication (VLC). The proposed method involves the sequential application of fingerprinting and double deep Q-Network. Fingerprinting is utilized to define the action and state of the double deep Q-Network agent. The agent is designed to learn and locate the reference point (RP) closest to the user’s position in a shorter search time. The core idea of the proposed system is to converge a Cell-ID scheme and fingerprinting. Through this, the initial state of the double deep Q-Network agent can be limited. A limited initial state can increase the positioning speed. Simulation results show that the proposed scheme attains a positioning resolution of less than 13 cm and achieves a processing time of less than 0.03 s to obtain the final position in VLC-based office environments.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14093684