Effective Adaptive Kalman Filter for MEMS-IMU/Magnetometers Integrated Attitude and Heading Reference Systems

To improve the computational efficiency and dynamic performance of low cost Inertial Measurement Unit (IMU)/magnetometer integrated Attitude and Heading Reference Systems (AHRS), this paper has proposed an effective Adaptive Kalman Filter (AKF) with linear models; the filter gain is adaptively tuned according to the dynamic scale sensed by accelerometers. This proposed approach does not need to model the system angular motions, avoids the non-linear problem which is inherent in the existing methods, and considers the impact of the dynamic acceleration on the filter. The experimental results with real data have demonstrated that the proposed algorithm can maintain an accurate estimation of orientation, even under various dynamic operating conditions.

[1]  Malcolm D. Shuster.  Survey of attitude representations , 1993 .

[2]  K.G. Blankinship.  A general theory for inertial navigator error modeling , 2008, 2008 IEEE/ION Position, Location and Navigation Symposium.

[3]  Jinling Wang,et al.  Quantization and Colored Noises Error Modeling for Inertial Sensors for GPS/INS Integration , 2011, IEEE Sensors Journal.

[4]  Alireza Khayatian,et al.  Attitude Estimation By Separate-Bias Kalman Filter-Based Data Fusion , 2004 .

[5]  Michael Zyda,et al.  NPSNET: Flight Simulation Dynamic Modeling Using Quaternions , 1992, Presence: Teleoperators & Virtual Environments.

[6]  M. Shuster.  A survey of attitude representation , 1993 .

[7]  Antoni Grau,et al.  Attitude and Heading System based on EKF total state configuration , 2011, 2011 IEEE International Symposium on Industrial Electronics.

[8]  Michael J. Caruso,et al.  Applications of Magnetoresistive Sensors in Navigation Systems , 1997 .

[9]  Satoru Emura,et al.  Sensor fusion based measurement of human head motion , 1994, Proceedings of 1994 3rd IEEE International Workshop on Robot and Human Communication.

[10]  Quang M. Lam,et al.  Gyro Modeling and Estimation of Its Random Noise Sources , 2003 .

[11]  P. Savage.  Strapdown Inertial Navigation Integration Algorithm Design Part 1: Attitude Algorithms , 1998 .

[12]  Andrew H. Jazwinski,et al.  Adaptive filtering , 1969, Autom..

[13]  M. Huber,et al.  MEMS IMU for AHRS applications , 2008, 2008 IEEE/ION Position, Location and Navigation Symposium.

[14]  M. J. Caruso,et al.  Applications of magnetic sensors for low cost compass systems , 2000, IEEE 2000. Position Location and Navigation Symposium (Cat. No.00CH37062).

[15]  J. Stuelpnagel.  On the Parametrization of the Three-Dimensional Rotation Group , 1964 .

[16]  Wei Li.  Dynamic Modelling for MEMS-IMU / Magnetometer Integrated Attitude and Heading Reference System , 2011 .

[17]  Yanhua Zhang,et al.  Adaptive filter for a miniature MEMS based attitude and heading reference system , 2004, PLANS 2004. Position Location and Navigation Symposium (IEEE Cat. No.04CH37556).

[18]  Jinling Wang,et al.  A Novel Method to Integrate IMU and Magnetometers in Attitude and Heading Reference Systems , 2011, Journal of Navigation.

[19]  Jiming Guo,et al.  Adaptive Kalman Filtering Based on Posteriori Variance-Covariance Components Estimation , 2011 .

[20]  Liang Xue,et al.  Fuzzy Adaptive Extended Kalman Filter for miniature Attitude and Heading Reference System , 2009, 2009 4th IEEE International Conference on Nano/Micro Engineered and Molecular Systems.

[21]  G. Wittum,et al.  Adaptive filtering , 1997 .

[22]  Drora Goshen-Meskin,et al.  Unified Approach to Inertial Navigation System Error Modeling , 1990 .

[23]  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.

[24]  F. Markley,et al.  Nonlinear Attitude Filtering Methods , 2005 .

[25]  Lin Qi,et al.  AHRS base on MEMS-IMU for aircraft model in wire-driven parallel suspension system , 2010, 2010 International Conference on Mechanic Automation and Control Engineering.

[26]  John B. Moore,et al.  Direct Kalman filtering approach for GPS/INS integration , 2002 .