A Study on Deep Reinforcement Learning Framework for DME Pulse Design
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이정연 | - |
dc.contributor.author | 김의호 | - |
dc.date.accessioned | 2021-09-02T03:41:35Z | - |
dc.date.available | 2021-09-02T03:41:35Z | - |
dc.date.created | 2021-08-18 | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 2288-8187 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/15835 | - |
dc.description.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. | - |
dc.language | 한국어 | - |
dc.language.iso | ko | - |
dc.publisher | 사단법인 항법시스템학회 | - |
dc.title | A Study on Deep Reinforcement Learning Framework for DME Pulse Design | - |
dc.title.alternative | A Study on Deep Reinforcement Learning Framework for DME Pulse Design | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 김의호 | - |
dc.identifier.doi | 10.11003/JPNT.2021.10.2.113 | - |
dc.identifier.bibliographicCitation | Journal of Positioning, Navigation, and Timing, v.10, no.2, pp.113 - 120 | - |
dc.relation.isPartOf | Journal of Positioning, Navigation, and Timing | - |
dc.citation.title | Journal of Positioning, Navigation, and Timing | - |
dc.citation.volume | 10 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 113 | - |
dc.citation.endPage | 120 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART002723485 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | distance measuring equipment (DME) | - |
dc.subject.keywordAuthor | alternative position | - |
dc.subject.keywordAuthor | navigation and timing (APNT) | - |
dc.subject.keywordAuthor | reinforcement learning | - |
dc.subject.keywordAuthor | deep learning | - |
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