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.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.11
- 수록면
- 1,287 - 1,293 (7page)
- DOI
- 10.5302/J.ICROS.2024.24.0190
이용수
초록· 키워드
오류제보하기
The Attitude and Heading Reference System (AHRS) plays a crucial role in various application fields, including unmanned vehicles, aerospace, and navigation. In an AHRS, 3D orientation, which encompasses both attitude and heading, is determined by processing signals from an inertial/magnetic sensor, which consists of an accelerometer, a gyroscope, and a magnetometer. Specifically, the accelerometer is used to directly determine attitude, while the magnetometer is employed for heading. Consequently, the accuracy of attitude and heading measurements is influenced by the error factors associated with the accelerometer and magnetometer. In this study, we analyze the impact of error factors in inertial/magnetic sensors on the accuracy of attitude and heading under undisturbed conditions, such as static environments and uniform magnetic fields. We compare attitude and heading errors from two orientation estimation algorithms (TRIAD and QUEST) based on various sensor error factors, including noise, bias, nonorthogonality, and misalignment errors. Noise density and bias instability are assessed through Allan deviation analysis, while nonorthogonal and misalignment errors are selected according to the specifications of the commercial AHRS module (MTw, Xsens, Netherlands). Our results present the root mean square errors (RMSEs) of attitude and heading for each individual error factor, as well as for various combinations of error factors. We found that sensor noise with a constant noise density produces attitude and heading errors that converge to a specific RMSE, whereas nonorthogonality and misalignment lead to significant deviations in errors depending on the sensor orientation. In terms of bias, the effects of bias instability were found to be negligible for zero constant bias. However, as the constant bias of the accelerometer and magnetometer increased by 1 mg and 0.5 mGauss, respectively, the attitude and heading errors increased by approximately 0.03°–0.04° and 0.04°, respectively.
목차
Abstract
I. 서론
II. 방법
III. 결과 및 고찰
IV. 결론
REFERENCES
참고문헌 (30)
참고문헌 신청
H. G. De Marina, F. J. Pereda, J. M. Giron-Sierra, and F. Espinosa, “UAV attitude estimation using unscented Kalman filter and TRIAD,” IEEE Transactions on Industrial Electronics, vol. 59, no. 11, pp. 4465-4474, 2011.
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Y. Wang, A. Hussain, and M. Soltani, “A MEMS-based adaptive AHRS for marine satellite tracking antenna,” IFAC-PapersOnLine, vol. 48, no. 16, pp. 121-126, 2015.
J. Choi, K. C. Marsim, M. Jeong, K. Ryoo, J. Kim, and H. Myung, “Multi-unmanned aerial vehicle pose estimation based on visual-inertial-range sensor fusion,” Journal of Institute of Control, Robotics and Systems (in Korean), vol. 29, no. 11, pp. 859-865, 2023.
D. Roetenberg, H. Luinge, and P. Slycke, “Xsens MVN: Full 6DOF human motion tracking using miniature inertial sensors,” Xsens Motion Technologies BV, Tech. Rep, vol. 1, 1-7, 2009.
E. K. Jørgensen, T. I. Fossen, T. H. Bryne, and I. Schjølberg, “Underwater position and attitude estimation using acoustic, inertial, and depth measurements,” IEEE Journal of Oceanic Engineering, vol. 45, no. 4, pp. 1450-1465, 2019.
Y. Wang, A. Hussain, and M. Soltani, “A MEMS-based adaptive AHRS for marine satellite tracking antenna,” IFAC-PapersOnLine, vol. 48, no. 16, pp. 121-126, 2015.
J. Choi, K. C. Marsim, M. Jeong, K. Ryoo, J. Kim, and H. Myung, “Multi-unmanned aerial vehicle pose estimation based on visual-inertial-range sensor fusion,” Journal of Institute of Control, Robotics and Systems (in Korean), vol. 29, no. 11, pp. 859-865, 2023.
D. Roetenberg, H. Luinge, and P. Slycke, “Xsens MVN: Full 6DOF human motion tracking using miniature inertial sensors,” Xsens Motion Technologies BV, Tech. Rep, vol. 1, 1-7, 2009.
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