Orientation Estimation Using a Quaternion-Based Indirect Kalman Filter With Adaptive Estimation of External Acceleration

This paper is concerned with orientation estimation using inertial and magnetic sensors. A quaternion-based indirect Kalman filter structure is used. The magnetic sensor output is only used for yaw angle estimation using two-step measurement updates. External acceleration is estimated from the residual of the filter and compensated by increasing the measurement noise covariance. Using the direction information of external information, the proposed method prevents unnecessarily increasing the measurement noise covariance corresponding to the accelerometer output, which is not affected by external acceleration. Through numerical examples, the proposed method is verified.

[1]  Xiaoping Yun,et al.  An investigation of the effects of magnetic variations on inertial/magnetic orientation sensors , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[2]  Xiaoping Yun,et al.  Limitations of Attitude Estimnation Algorithms for Inertial/Magnetic Sensor Modules , 2007, IEEE Robotics & Automation Magazine.

[3]  James K. Hall,et al.  Quaternion attitude estimation for miniature air vehicles using a multiplicative extended Kalman filter , 2008, 2008 IEEE/ION Position, Location and Navigation Symposium.

[4]  Greg Welch,et al.  Motion Tracking: No Silver Bullet, but a Respectable Arsenal , 2002, IEEE Computer Graphics and Applications.

[5]  Y. Suh,et al.  Attitude Estimation Adaptively Compensating External Acceleration , 2006 .

[6]  Robert B. McGhee,et al.  A Simplified Quaternion-Based Algorithm for Orientation Estimation From Earth Gravity and Magnetic Field Measurements , 2008, IEEE Transactions on Instrumentation and Measurement.

[7]  Eric Foxlin,et al.  Inertial head-tracker sensor fusion by a complementary separate-bias Kalman filter , 1996, Proceedings of the IEEE 1996 Virtual Reality Annual International Symposium.

[8]  Esmat Bekir Introduction to Modern Navigation Systems , 2007 .

[9]  R.P.G. Collinson Introduction to avionics systems, 2nd edition - Book Review , 2004, IEEE Circuits and Devices Magazine.

[10]  Jack B. Kuipers,et al.  Quaternions and Rotation Sequences: A Primer with Applications to Orbits, Aerospace and Virtual Reality , 2002 .

[11]  E. J. Lefferts,et al.  Kalman Filtering for Spacecraft Attitude Estimation , 1982 .

[12]  R. P. G. Collinson,et al.  Introduction to Avionics Systems , 2003 .

[13]  F. Landis Markley,et al.  Kalman Filter for Spinning Spacecraft Attitude Estimation , 2007 .

[14]  W. F. Phillips,et al.  Review of Attitude Representations Used for Aircraft Kinematics , 2001 .

[15]  Xiaoping Yun,et al.  Design, Implementation, and Experimental Results of a Quaternion-Based Kalman Filter for Human Body Motion Tracking , 2006, IEEE Trans. Robotics.

[16]  Peter S. Maybeck,et al.  Stochastic Models, Estimation And Control , 2012 .

[17]  Angelo M. Sabatini,et al.  Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing , 2006, IEEE Transactions on Biomedical Engineering.

[18]  P. O. Collinson Year 2000 and the laboratory: embedded chips, algorithms and information systems , 1999 .

[19]  A. G. Sreenatha,et al.  Attitude Dynamics Identification of Unmanned Aircraft Vehicle , 2006 .

[20]  Zhaoying Zhou,et al.  A Small Low-Cost Hybrid Orientation System and Its Error Analysis , 2009 .

[21]  M. Shuster,et al.  Three-axis attitude determination from vector observations , 1981 .

[22]  Jung-Keun Lee,et al.  A minimum-order kalman filter for ambulatory real-time human body orientation tracking , 2009, 2009 IEEE International Conference on Robotics and Automation.

[23]  Richard A. Brown,et al.  Introduction to random signals and applied kalman filtering (3rd ed , 2012 .

[24]  Angelo M. Sabatini,et al.  Assessment of walking features from foot inertial sensing , 2005, IEEE Transactions on Biomedical Engineering.