Abstract
This paper focuses on the approach to attitude estimation. When describing the attitude of a dynamic object, the quaternion has a better numerical continuity and stability compared with other conventional Euler angles’ methods. However, acceleration observation vector has a nonlinear relation to attitude quaternion and as a result, the representative linear methods e.g. Kalman filter (KF) and complementary filter (CF) are no longer applicable. In addition, the disturbances of accelerometers and magnetometers also greatly degrade the attitude estimation reliability, leading to solution biased even divergence. In this contribution, the general heterogeneous MARG data fusion strategy is proposed, to minimize the noises influences of nonlinear system imposing on the attitude estimation of MARG sensors. To overcome the nonlinear estimation problem, the unscented Kalman filter (UKF) for attitude determination is proposed based on the unscented transformation. Furthermore, a real-time disturbance detection rules are established for the external acceleration and magnetic field distortion. Finally, the real experiments are carried out to evaluate performances of our proposed attitude estimation method.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bird J, Arden D (2011) Indoor navigation with foot-mounted strap down inertial navigation and magnetic sensors emerging opportunities for localization and tracking. Wirel Commun IEEE 18(2):28–35
Choukroun D, Bar-Itzhack IY, Oshman Y (2002) Novel quaternion Kalman filter. IEEE Trans Aerosp Electron Syst 42(1):174–190
Gao N, Wang M, Zhao L (2017) A novel robust Kalman filter on AHRS in the magnetic distortion environment. Adv Space Res 60(12):2630–2636
Feng K, Li J, Zhang X, Shen C, Bi Y, Zheng T, Liu J (2017) A new quaternion-based Kalman filter for real-time attitude estimation using the two-step geometrically-intuitive correction algorithm. Sensors 17(9):2146
Ghasemi-Moghadam S, Homaeinezhad MR (2018) Attitude determination by combining arrays of MEMS accelerometers, gyros, and magnetometers via quaternion-based complementary filter. Int J Numer Model-Electron Netw Devices Fields 31(3):e2282
Hemerly EM, Maciel BCO, Milhan ADP, Schad VR (2012) Attitude and heading reference system with acceleration compensation. Aircr Eng Aerosp Technol 84(2):87–93
Jalali M, Hashemi E, Khajepour A, Chen SK, Litkouhi B (2017) Integrated model predictive control and velocity estimation of electric vehicles. Mechatronics 46:84–100
Koo W, Sung S, Lee YJ (2009) Development of real-time heading estimation algorithm using magnetometer/IMU. ICCAS-SICE, pp 4212–4216
Kottath R, Narkhede P, Kumar V, Karar V, Poddar S (2017) Multiple model adaptive complementary filter for attitude estimation. Aerosp Sci Technol 70:644
Lee JK, Park EJ, Robinovitch SN (2012) Estimation of attitude and external acceleration using inertial sensor measurement during various dynamic conditions. IEEE Trans Instrum Meas 61(8):2262–2273
Lee H, Jung S (2012) Balancing and navigation control of a mobile inverted pendulum robot using sensor fusion of low cost sensors. Mechatronics 22(1):95–105
Li X, Li Q (2017) External acceleration elimination for complementary attitude filter. In: IEEE international conference on information and automation, pp 208–212
Luinge HJ, Veltink PH (2005) Measuring orientation of human body segments using miniature gyroscopes and accelerometers. Med Biol Eng Compu 43(2):273–282
Markley FL (1993) Attitude determination from vector observations: a fast optimal matrix algorithm. J Astronaut Sci 41(2):261–280
Shuster MD, Oh SD (1981) Three-axis attitude determination from vector observations. J Guidance Control Dyn 4(1):70–77
Suh YS (2010) Orientation estimation using a quaternion-based indirect Kalman filter with adaptive estimation of external acceleration. IEEE Trans Instrum Meas 59(12):3296–3305
Odry Á, Fullér R, Rudas IJ, Odry P (2018) Kalman filter for mobile-robot attitude estimation: novel optimized and adaptive solutions. Mech Syst Sig Process 110:569–589
Wang Y, Li N, Chen X, Liu M (2014) Design and implementation of an AHRS based on MEMS sensors and complementary filtering. Adv Mech Eng. https://doi.org/10.1155/2014/214726
Wu J, Zhou Z, Chen J, Fourati H, Li R (2016a) Fast complementary filter for attitude estimation using low-cost MARG sensors. IEEE Sens J 16(18):6997–7007
Wu J, Zhou Z, Gao B, Li R, Cheng Y, Fourati H (2018) Fast linear quaternion attitude estimator using vector observations. IEEE Trans Autom Sci Eng 15(1):307–319
Wu Z, Sun Z, Zhang W, Chen Q (2016b) A novel approach for attitude estimation based on MEMS inertial sensors using nonlinear complementary filters. IEEE Sens J 16(10):3856–3864
Yang Q, Sun L, Yang L (2018) A fast adaptive-gain complementary filter algorithm for attitude estimation of an unmanned aerial vehicle. J Navig 71(6):1477
Zhang Z, Zhou Z, Wu J, Du S, Fourati H (2018) Fast linear attitude estimation and angular rate generation. Indoor Positioning Indoor Navig. https://doi.org/10.1109/ipin.2018.8533736
Zhou Z, Li Y, Liu J, Li G (2013) Equality constrained robust measurement fusion for adaptive Kalman filter based heterogeneous multi-sensor navigation. IEEE Trans Aerosp Electron Syst 49(4):2146–2157
Zhou Z, Wu J, Wang J, Fourati H (2018) Optimal recursive and sub-optimal linear solutions to attitude determination from vector observations for GNSS/Accelerometer/Magnetometer orientation measurement. Remote Sens 10(3):1–28
Acknowledgment
This work is supported by the National Natural Science Funds of China (No. 41604025 and 41704029), the State Key Laboratory of Geodesy and Earth’s Dynamics (Institute of Geodesy and Geophysics, CAS, SKLGED2018-3-2E), Sichuan Province Science and Technology Project (No. 2018CC0018; 2018SZ0364) and the Fundamental Research Funds for the Central Universities under Grant ZYGX2018J080.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, Z., Zhou, Z., Du, S., Xiang, C., Kuang, C. (2019). Unscented Kalman Filter Based Attitude Estimation with MARG Sensors. In: Sun, J., Yang, C., Yang, Y. (eds) China Satellite Navigation Conference (CSNC) 2019 Proceedings. CSNC 2019. Lecture Notes in Electrical Engineering, vol 563. Springer, Singapore. https://doi.org/10.1007/978-981-13-7759-4_43
Download citation
DOI: https://doi.org/10.1007/978-981-13-7759-4_43
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-7758-7
Online ISBN: 978-981-13-7759-4
eBook Packages: EngineeringEngineering (R0)