A Parallel Attitude-Heading Kalman Filter Without State-Augmentation of Model-Based Disturbance Components

This paper proposes an inertial and magnetic sensing-based parallel orientation Kalman filter (KF), which is composed of two separate KFs: one for attitude estimation with an external acceleration model as presented in author’s previous work and the other for heading estimation with a magnetic disturbance model. The novelty of the proposed attitude-heading KF, compared to other model-based KFs, is that the disturbance models are implemented by inserting them into the measurement equations without state-augmentation. In this case, there is no increase in the matrix size of the corresponding KF structure, which is advantageous as the computational cost is approximately halved when compared with the state-augmentation type approaches. The proposed KF has been experimentally verified in magnetically disturbed conditions. The proposed method has advantages over the KF with state-augmentation in the calculation efficiency and ease of use.

[1]  Angelo M. Sabatini,et al.  A Novel Kalman Filter for Human Motion Tracking With an Inertial-Based Dynamic Inclinometer , 2015, IEEE Transactions on Biomedical Engineering.

[2]  Hee-Jun Kang,et al.  Quaternion-Based Indirect Kalman Filter Discarding Pitch and Roll Information Contained in Magnetic Sensors , 2012, IEEE Transactions on Instrumentation and Measurement.

[3]  Angelo M. Sabatini.  Variable-State-Dimension Kalman-Based Filter for Orientation Determination Using Inertial and Magnetic Sensors , 2012, Sensors.

[4]  P. Veltink,et al.  Compensation of magnetic disturbances improves inertial and magnetic sensing of human body segment orientation , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[5]  Jung-Keun Lee,et al.  Estimation of Attitude and External Acceleration Using Inertial Sensor Measurement During Various Dynamic Conditions , 2012, IEEE Transactions on Instrumentation and Measurement.

[6]  Muhammad Tahir,et al.  Improving the Accuracy of Human Body Orientation Estimation With Wearable IMU Sensors , 2017, IEEE Transactions on Instrumentation and Measurement.

[7]  Angelo M. Sabatini,et al.  A linear Kalman Filtering-based approach for 3D orientation estimation from Magnetic/Inertial sensors , 2015, 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI).

[8]  Jung-Keun Lee,et al.  A Fast Quaternion-Based Orientation Optimizer via Virtual Rotation for Human Motion Tracking , 2009, IEEE Transactions on Biomedical Engineering.