International Journal of Aeronautical and Space Sciences

The Korean Society for Aeronautical and Space Sciences (한국항공우주학회)
권호 표지
DOI QR코드

DOI QR Code

Vibration-Robust Attitude and Heading Reference System Using Windowed Measurement Error Covariance

  • Received : 2016.05.11
  • Accepted : 2017.07.18
  • Published : 2017.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, a new technique for attitude and heading reference system (AHRS) using low-cost MEMS sensors of the gyroscope, accelerometer, and magnetometer is addressed particularly in vibration environments. The motion of MEMS sensors interact with the scale factor and cross-coupling errors to produce random errors by the harsh environment. A new adaptive attitude estimation algorithm based on the Kalman filter is developed to overcome these undesirable side effects by analyzing windowed measurement error covariance. The key idea is that performance degradation of accelerometers, for example, due to linear vibrations can be reduced by the proposed measurement error covariance analysis. The computed error covariance is utilized to the measurement covariance of Kalman filters adaptively. Finally, the proposed approach is verified by using numerical simulations and experiments in an acceleration phase and/or vibrating environments.

Keywords

Acknowledgement

Supported by : National Research Foundation

References

  1. Cho, A., Kim, J., Lee, S., Choi, S., Lee, B., Kim, B. and Kee, C., "Fully Automatic Taxiing, Takeoff and Landing of a UAV Using a Single-Antenna GPS Receiver Only", Control, Automation and Systems, ICCAS'07. International Conference on. IEEE, 2007.
  2. Babu, R. and Wang, J., "Improving the Quality of IMU-Derived Doppler Estimates for Ultra-Tight GPS/INS Integration", GNSS2004, 2004, pp. 16-19.
  3. Sabatini, A. M., "Quaternion-Based Extended Kalman Filter for Determining Orientation by Inertial and Magnetic Sensing", IEEE Transactions on Biomedical Engineering, Vol. 53, No. 7, 2006, pp. 1346-1356. https://doi.org/10.1109/TBME.2006.875664
  4. Rong, Z., et al. "A Linear Fusion Algorithm for Attitude Determination Using Low Cost MEMS-based Sensors." Measurement, Vol. 40 No. 3, 2007, pp. 322-328. https://doi.org/10.1016/j.measurement.2006.05.020
  5. Lai, Y.-C., Jan, S.-S., and Hsiao, F.-B., "Development of A Low-cost Attitude and Heading Reference System Using A Three-axis Rotating Platform." Sensors, Vol. 10, No. 4, 2010, pp. 2472-2491. https://doi.org/10.3390/s100402472
  6. Roetenberg, D., et al. "Compensation of Magnetic Disturbances Improves Inertial and Magnetic Sensing of Human Body Segment Orientation." Neural Systems and Rehabilitation Engineering, IEEE Transactions on, Vol. 13, No. 3, 2005. pp. 395-405. https://doi.org/10.1109/TNSRE.2005.847353
  7. Foxlin, E., "Inertial Head-tracker Sensor Fusion by A Complementary Separate-bias Kalman Filter." Virtual Reality Annual International Symposium, Proceedings of the IEEE 1996, 1996.
  8. Sabatini, A. M., "Quaternion-based Extended Kalman Filter for Determining Orientation by Inertial and Magnetic Sensing." Biomedical Engineering, IEEE Transactions on, Vol. 53 No. 7, 2006, pp. 1346-1356. https://doi.org/10.1109/TBME.2006.875664
  9. Gebre-Egziabher, D., Hayward, R. C., and Powell, J. D., "Design of Multi-sensor Attitude Determination Systems." Aerospace and Electronic Systems, IEEE Transactions on, Vol. 40 No. 2, 2004, pp. 627-649.. https://doi.org/10.1109/TAES.2004.1310010
  10. Li, W., and Wang, J., "Effective Adaptive Kalman Filter for MEMS-IMU/Magnetometers Integrated Attitude and Heading Reference Systems." Journal of Navigation, Vol. 66, No.01, 2013, pp. 99-113. https://doi.org/10.1017/S0373463312000331
  11. Calusdian, J., Yun, X., and Bachmann, E., "Adaptivegain Complementary Filter of Inertial and Magnetic Data for Orientation Estimation." Robotics and Automation (ICRA), 2011 IEEE International Conference on. IEEE, 2011.
  12. Crassidis, J. L., and Junkins, J. L., Optimal Estimation of Dynamic Systems, CRC press, 2011.
  13. Trawny, N., and Roumeliotis, S. I., "Indirect Kalman Filter for 3D Attitude Estimation." University of Minnesota, Dept. of Comp. Sci. & Eng., Tech. Rep 2, 2005.
  14. Yun, X., et al. "An Improved Quaternion-based Kalman Filter for Real-time Tracking of Rigid Body Orientation." Intelligent Robots and Systems, 2003.(IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference on. Vol. 2. IEEE, 2003.
  15. Diebel, J., "Representing Attitude: Euler angles, Unit Quaternions, and Rotation Vectors." Matrix, Vol.58 No.15-16, 2006, pp. 1-35.
  16. Pethel, S. J., "Test and Evaluation of High Performance Micro Electro-mechanical System Based Inertial Measurement Units." Position, Location, And Navigation Symposium, 2006 IEEE/ION. IEEE, 2006.
  17. Groves, P. D., Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems. Artech house, 2013.