인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
A magnetometer should be calibrated to correct errors such as offset, scale factor, misalignment soft iron distortion, and hard iron distortion so as to achieve accurate azimuth estimation. An outlier elimination method has been recently proposed to improve calibration performance by eliminating the outliers of calibration data. In this method, it is assumed that calibration data is collected in a uniform distribution to form a sphere. However, considering the actual data collection process, it is difficult to guarantee uniformly distributed data. Thus, this study investigates the effects of outlier elimination on the performance of ellipsoid fitting-based magnetometer calibration. In this study, the calibration performances before and after outlier elimination were compared for the following three cases: uniformly distributed data with insignificant outliers (Case 1), uniformly distributed data with significant outliers (Case 2), and nonuniformly distributed data (Case 3). In addition, the calibration performances were compared according to a threshold for detecting outliers. The results of this study showed that outlier elimination changed the azimuth estimation performance by -0.002°, 0.711°, and -2.588° for Cases 1–3, respectively, in terms of root mean squared error. Overall, these results indicate that outlier elimination is effective for uniformly distributed data with significant outliers; however, it causes performance degradation for nonuniformly distributed data. Therefore, it is essential to examine the distribution of calibration data before applying outlier elimination.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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