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초록
Recently, research has been actively conducted to overcome the limitations of high-priced single sensors and reduce costs through the convergence of low-cost multi-variable sensors. This paper estimates state variables through asynchronous Kalman filters constructed using CVXPY and uses Cvxpylayers to compare and learn state variables estimated from CVXPY with true value data to estimate filter parameters of low-cost sensors fusion.
키워드
Asynchronous Filter; Cvxpylayers; Inertial Measurement Unit(IMU); Machine Learning; Sensor Fusion
- 제목
- 관성/고도 센서 융합을 위한 기계학습기반 필터 파라미터 추정
- 제목 (타언어)
- Machine Learning-Based Filter Parameter Estimation for Inertial/Altitude Sensor Fusion
- 저자
- 황현수; 김효중; 이학태; 김종한
- 발행일
- 2023-12
- 유형
- Y
- 저널명
- 한국항행학회논문지
- 권
- 27
- 호
- 6
- 페이지
- 884 ~ 887