Signal processing of landing radar considering irradiated surface characteristics using convolutional neural networks

Moeko Hidaka, Masaki Takahashi, Takayuki Ishida, Kazuki Kariya, Takahide Mizuno, Seisuke Fukuda

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

In this paper, a signal-processing method for a lunar lander using deep learning is proposed. The ability for pinpoint soft landing on a lunar/planetary surface broadens the range of scientific and exploration missions. To perform pinpoint landing, measurement of the relative velocity with respect to the surface is essential. Landing radar is a sensor that measures the relative velocity. To measure the velocity, the landing radar irradiates the surface with a pulse wave and observes the Doppler shift. High-precision measurement on complex terrains, a crater, or a slope has always been the problem of landing radar because the irradiated terrains strongly affect the accuracy. We propose a measurement system that performs with high accuracy on complex terrains using convolutional neural networks. Moreover, we confirm that the proposed method could improve the measurement accuracy compared with the existing method.

本文言語English
ホスト出版物のタイトルAIAA Scitech 2019 Forum
出版社American Institute of Aeronautics and Astronautics Inc, AIAA
ISBN(印刷版)9781624105784
DOI
出版ステータスPublished - 2019
イベントAIAA Scitech Forum, 2019 - San Diego, United States
継続期間: 2019 1 72019 1 11

出版物シリーズ

名前AIAA Scitech 2019 Forum

Conference

ConferenceAIAA Scitech Forum, 2019
国/地域United States
CitySan Diego
Period19/1/719/1/11

ASJC Scopus subject areas

  • 航空宇宙工学

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