A Study on Deep Reinforcement Learning Framework for DME Pulse DesignA Study on Deep Reinforcement Learning Framework for DME Pulse Design
- Other Titles
- A Study on Deep Reinforcement Learning Framework for DME Pulse Design
- Authors
- 이정연; 김의호
- Issue Date
- 2021
- Publisher
- 사단법인 항법시스템학회
- Keywords
- distance measuring equipment (DME); alternative position; navigation and timing (APNT); reinforcement learning; deep learning
- Citation
- Journal of Positioning, Navigation, and Timing, v.10, no.2, pp.113 - 120
- Journal Title
- Journal of Positioning, Navigation, and Timing
- Volume
- 10
- Number
- 2
- Start Page
- 113
- End Page
- 120
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/15835
- DOI
- 10.11003/JPNT.2021.10.2.113
- ISSN
- 2288-8187
- Abstract
- The Distance Measuring Equipment (DME) is a ground-based aircraft navigation system and is considered as an infrastructure that ensures resilient aircraft navigation capability during the event of a Global Navigation Satellite System (GNSS) outage. The main problem of DME as a GNSS back up is a poor positioning accuracy that often reaches over 100 m. In this paper, a novel approach of applying deep reinforcement learning to a DME pulse design is introduced to improve the DME distance measuring accuracy. This method is designed to develop multipath-resistant DME pulses that comply with current DME specifications. In the research, a Markov Decision Process (MDP) for DME pulse design is set using pulse shape requirements and a timing error. Based on the designed MDP, we created an Environment called PulseEnv, which allows the agent representing a DME pulse shape to explore continuous space using the Soft Actor Critical (SAC) reinforcement learning algorithm.
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Collections - College of Engineering > Department of Mechanical and System Design Engineering > 1. Journal Articles
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