Jaw-Kuen Shiau This email address is being protected from spambots. You need JavaScript enabled to view it.1 and I-Chiang Wang1

1Department of Aerospace Engineering, Tamkang University, Danshui, New Taipei City 25137, Taiwan, R.O.C.


 

Received: June 27, 2012
Accepted: September 21, 2012
Publication Date: June 1, 2013

Download Citation: ||https://doi.org/10.6180/jase.2013.16.2.08  


ABSTRACT


This paper presents the results of a quaternion-based unscented Kalman filtering for attitude estimation using low cost MEMS sensors. The unscented Kalman filter uses the pitch and roll angles computed from gravity force decomposition as the measurement for the filter. The immeasurable gravity accelerations are deduced from the outputs of the three axes accelerometers, the relative accelerations, and the accelerations due to body rotation. The constraint of the four elements of the quaternion method is treated as a perfect measurement and is integrated into the system to form a constrained unscented Kalman filter. The heading angle is obtained from a complimentary filter which uses the heading signal derived from the magnetic force information from an electronic magnetic sensor and the GPS-derived heading as the inputs. An experiment using an in-house designed motion platform is conducted to evaluate the proposed algorithm. The noise characteristics of the sensor signals are examined using the laboratory data. Approximations of the time-varying noise variances of the measured signals are obtained through Taylor series expansions. The algorithm is intuitive and easy to implement. Moreover, the proposed algorithm and the filter design are successfully demonstrated through a complete set of flight test data.


Keywords: Flight Information Measurement, Nonlinear Kalman Filter, Quaternion, Complementary Filter


REFERENCES


  1. [1] Demoz, G. E., “A Low-Cost GPS/Inertial Attitude Heading Reference System (AHRS) for General Aviation Applications,” Proc. of IEEE 1998 Position Location and Navigation Symposium, pp. 518525 (1998). doi: 10.1109/PLANS.1998.670207
  2. [2] Demoz, G. E., Elkaim, G. H., Powell, J. D. and Parkison, B. W., “A Gyro-Free Quaternion-Based Attitude Determination System Suitable for Implementation Using Low Cost Sensors,” Proc. of 2000 IEEE Position Location and Navigation Symposium, pp. 185192 (2000). doi: 10.1109/PLANS.2000.838301
  3. [3] Wang, L., Xiong, S., Zhou, Z., Wei, Q. and Lan, J., “Constrained Filtering Method for MAV Attitude Determination,” Proc. of IEEE 2005 Instrumentation and Measurement Technology Conference, pp. 14801483 (2005). doi: 10.1109/IMTC.2005.1604397
  4. [4] Euston, M., Coote, P., Mahony, R., Kim, J. and Hamel, T., “A Complementary Filter for Attitude Estimation of a Fixed-Wing UAV,” Proc. of 2008 IROS, IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 340345 (2008). doi: 10.1109/IROS. 2008.4650766
  5. [5] Yoo, T. S., Hong, S. K., Yoon, H. M. and Pa, S., “Gain-Scheduled Complementary Filter Design for a MEMS Based Attitude and Heading Reference System”, Sensors, Vol. 11, pp. 38163830 (2011). doi: 10.3390/s110403816
  6. [6] Zhu, R., Sun, D., Zhou, Z. and Wang, D., “A Linear Fusion Algorithm for Attitude Determination Using Low Cost MEMS-Based Sensors,” Measurement, Vol. 40, pp. 322328 (2007). doi: 10.1016/j.measurement. 2006.05.020
  7. [7] Hong, S. K. “Fuzzy Logic Based Closed-Loop Strapdown Attitude System for Unmanned Aerial Vehicle (UAV),” Sensors and Actuators A: Physical, Vol. 107, pp. 109118 (2003). doi: 10.1016/S0924-4247(03) 00353-4
  8. [8] Wang, M., Yang, Y., Ronald, R. H. and Zhang, Y., “Adaptive Filter for a Miniature MEMS Based Attitude and Heading Reference System,” Proc. of 2004 IEEE, Position Location and Navigation Symposium, pp. 193200 (2004). doi: 10.1109/PLANS.2004. 1308993
  9. [9] Lam, Q. M. and Crassidis, J. L., “Precision Attitude Determination Using a Multiple Model Adaptive Estimation Scheme,” Proc. of 2007 IEEE, Aerospace Conference, pp. 120 (2007). doi: 10.1109/AERO.2007. 352657
  10. [10] Crassidis, J. L. and Markley, F. L., “Unscented Filtering for Spacecraft Attitude Estimation,” Journal of Guidance Control and Dynamics, Vol. 26, No. 4, pp. 536542 (2003). doi: 10.2514/2.5102
  11. [11] Shin, E. H. and El-Sheimy, N., “An Unscented Kalman Filter for In-Motion Alignment of Low-Cost IMUs,” Proc. of 2004 Position Location and Navigation Symposium, pp. 273279 (2004). doi: 10.1109/PLANS. 2004.1309005
  12. [12] VanDyke, M. C., Schwartz, J. L. and Hall, C. D., “Unscented Kalman Filtering for Spacecraft Attitude State and Parameter Estimation,” Proc. of the AAS/AIAA Space Flight Mechanics Conference, No. AAS 04-115 (2004).
  13. [13] Zhao, L., Nie, Q. and Guo, Q., “Unscented Kalman Filtering for SINS Attitude Estimation,” Proc. of 2007 IEEE International Conference on Control and Automation, pp. 228232 (2007). doi: 10.1109/ICCA. 2007.4376353
  14. [14] Shiau, J.-K., Ma, D.-M., Wu, T.-H. and Huang, L.-H., “Design of a MEMS-Based Flight Information Measurement Unit for UAV Application”, Journal of Emerging Trends in Engineering and Applied Sciences, Vol. 2, No. 2, pp. 197204 (2011).
  15. [15] Baruh, H., Analytical Dynamics, McGraw-Hill (1999).
  16. [16] Collinson, R. P. G., Introduction To Avionics, CHAPMAN & HALL (1996).
  17. [17] Simon, D., Optimal State Estimation, Wiley (2006)


    
 

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