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Korea Society for Naval Science and Technology - Vol. 7 , No. 1

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[ Article ]
Journal of the KNST - Vol. 7, No. 1, pp. 85-91
Abbreviation: KNST
ISSN: 2635-4926 (Print)
Print publication date 31 Mar 2024
Received 27 Dec 2023 Revised 15 Jan 2024 Accepted 05 Mar 2024
DOI: https://doi.org/10.31818/JKNST.2024.3.7.1.85
저가 관성센서를 이용한 AUV의 수중 발사 초기 기동특성 기반 자세계산
현철¹^{, *} ; 김현승² ; 김창환¹ ; 이호진¹

1LIG넥스원 전장분석개발팀 수석연구원

2LIG넥스원 전장분석개발팀 선임연구원
Attitude Estimation of AUV Based on Initial Maneuvering Characteristics Using Low-cost Inertial Sensors
Chul Hyun¹^{, *} ; Hyunseung Kim² ; Changhwan Kim¹ ; Hojin Lee¹
1Principal researcher, Dept. of Battlefield Analysis and Development, LIG Nex1

2Senior researcher, Dept. of Battlefield Analysis and Development, LIG Nex1

Correspondence to : ^*Chul Hyun Dept. of Battlefield Analysis and Development, LIG Nex1 333 Pangyo-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, 13488, Republic of Korea Tel: +82-31-5178-4293 Fax: +82-31-5179-7086 E-mail: chul.hyun@gmail.com
Ⓒ 2024 Korea Society for Naval Science & Technology

초록

본 논문에서는 수중운동체의 초기 발사 안정화 단계의 기동 특성을 반영한 자세계산 방법을 검토하였다. 저가 관성센서인 가속도계와 자이로의 오차 모델링 포함 측정값을 기준으로 기동 상태를 판단하고, 가중함수를 이용한 통합 자세 추정치를 획득했다. 발사 후 20초간의 몬테카를로 시뮬레이션을 수행한 결과, 제시한 방법의 자세오차 RMS값이 전 구간에 대하여 1도 미만의 값으로 양호한 결과를 얻을 수 있었다.

Abstract

In this paper, we proposed an attitude estimation method that reflects the maneuvering characteristics of the initial launch stabilization stage of an underwater vehicle. The maneuver state was determined based on the measurement values including the error modeling of the low-cost accelerometers and gyroscopes. The integrated attitude estimate was obtained using a weighting function. As a result of performing a Monte Carlo simulation for 20 seconds after launch, the RMS value of the attitude error of the proposed method was obtained as less than 1 degree for the entire range, which is a good result.


Keywords: Autonomous Underwater Vehicle, MEMS Inertial Sensor, Attitude Reference System, Attitude Estimation 키워드: 자율수중운동체, MEMS 관성센서, 자세기준장치, 자세 추정

References


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*10.*	S. J. Park, J. Park, and J. W. Song, “Attitude Estimation Algorithm Using Adaptive Complementary Filter Based on Deep Reinforcement Learning,” Proceeding of ICROS 2021, pp.139-140.