Discrete time-varying attitude complementary filter

This paper presents the development of an attitude complementary filter for an Attitude and Heading Reference System (AHRS). Using strapdown inertial measurements and vector observations, the proposed complementary filter provides attitude estimates in Euler angles representation, while compensating for rate gyro bias. Stability and performance properties of the proposed filter under operating conditions usually found in oceanic applications are derived, and the tuning of the filter parameters in the frequency domain is emphasized. The proposed solution poses small computational requirements, and is suitable for implementation on low-power hardware using low-cost sensors. Experimental results obtained with an implementation of the algorithm running on-board the DELFIMx catamaran are presented and discussed.

[1]  Walter Higgins,et al.  A Comparison of Complementary and Kalman Filtering , 1975, IEEE Transactions on Aerospace and Electronic Systems.

[2]  G. Wahba A Least Squares Estimate of Satellite Attitude , 1965 .

[3]  John L. Crassidis,et al.  Survey of nonlinear attitude estimation methods , 2007 .

[4]  R. G. Brown Integrated Navigation Systems and Kalman Filtering: A Perspective , 1972 .

[5]  Phillip J. McKerrow,et al.  Introduction to robotics , 1991 .

[6]  Kenneth R Britting,et al.  Inertial navigation systems analysis , 1971 .

[7]  ten Josephus Berge,et al.  Review of: J.C. Gower & G.B. Dijksterhuis: Procrustes Problems, Oxford University Press. , 2004 .

[8]  Roy M. Howard,et al.  Linear System Theory , 1992 .

[9]  Shmuel Merhav Aerospace Sensor Systems and Applications , 1998 .

[10]  J.L. Crassidis,et al.  Sigma-point Kalman filtering for integrated GPS and inertial navigation , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[11]  Hugh F. Durrant-Whyte,et al.  The aiding of a low-cost strapdown inertial measurement unit using vehicle model constraints for land vehicle applications , 2001, IEEE Trans. Robotics Autom..

[12]  Guanrong Chen,et al.  Introduction to random signals and applied Kalman filtering, 2nd edn. Robert Grover Brown and Patrick Y. C. Hwang, Wiley, New York, 1992. ISBN 0‐471‐52573‐1, 512 pp., $62.95. , 1992 .

[13]  B. Anderson,et al.  Digital control of dynamic systems , 1981, IEEE Transactions on Acoustics, Speech, and Signal Processing.

[14]  B. Anderson,et al.  Optimal Filtering , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[15]  Bijoy K. Ghosh,et al.  Pose estimation using line-based dynamic vision and inertial sensors , 2003, IEEE Trans. Autom. Control..

[16]  Wilson J. Rugh,et al.  Linear system theory (2nd ed.) , 1996 .

[17]  Francis L. Merat,et al.  Introduction to robotics: Mechanics and control , 1987, IEEE J. Robotics Autom..

[18]  Norbert Wiener,et al.  Extrapolation, Interpolation, and Smoothing of Stationary Time Series , 1964 .

[19]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[20]  F. Markley Attitude Error Representations for Kalman Filtering , 2003 .

[21]  John J. Craig,et al.  Introduction to robotics - mechanics and control (2. ed.) , 1989 .

[22]  Robert E. Mahony,et al.  Nonlinear Complementary Filters on the Special Orthogonal Group , 2008, IEEE Transactions on Automatic Control.

[23]  Rita Cunha,et al.  A Coastline Following Preview Controller For the DELFIMx Vehicle , 2007 .