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http://dx.doi.org/10.11003/JPNT.2021.10.2.113

A Study on Deep Reinforcement Learning Framework for DME Pulse Design  

Lee, Jungyeon (Mechanical System and Design Engineering, Hongik University)
Kim, Euiho (Mechanical System and Design Engineering, Hongik University)
Publication Information
Journal of Positioning, Navigation, and Timing / v.10, no.2, 2021 , pp. 113-120 More about this Journal
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.
Keywords
distance measuring equipment (DME); alternative position; navigation and timing (APNT); reinforcement learning; deep learning;
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