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A Fast Adaptive-Gain Complementary Filter Algorithm for Attitude Estimation of an Unmanned Aerial Vehicle

Published online by Cambridge University Press:  21 May 2018

Qing-quan Yang ,
Ling-ling Sun  and
Longzhao Yang
Qing-quan Yang
Affiliation:
(College of electrical engineering, Zhejiang University, Hangzhou 310027, China)
Ling-ling Sun*
Affiliation:
(College of electrical engineering, Zhejiang University, Hangzhou 310027, China) (School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310027, China)
Longzhao Yang
Affiliation:
(School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310027, China)
*
(E-mail: sunll@hdu.edu.cn)
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Abstract

A novel fast adaptive-gain complementary filter algorithm is developed for Unmanned Aerial Vehicle (UAV) attitude estimation. This approach provides an accurate, robust and simple method for attitude estimation with minimised attitude errors and reduced computation. UAV attitude data retrieved from accelerometer data is transformed to the solution of a linearly discrete dynamic system. A novel complementary filter is designed to fuse accelerometer and gyroscope data, with a self-adjusted gain to achieve a good performance in accuracy. The performance of the proposed algorithm is compared with an Adaptive-gain Complementary Filter (ACF) and Extended Kalman Filtering (EKF). Simulation and experimental results show that the accuracy of the proposed filter has the same performance as an EKF in high dynamic operating conditions. Therefore, the proposed algorithm can balance accuracy and time consumption, and it has a better price/performance ratio in engineering applications.

Keywords

Attitude estimationComplementary filterIMU sensorsUnmanned Aerial Vehicle
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 6 , November 2018 , pp. 1478 - 1491
Copyright
Copyright © The Royal Institute of Navigation 2018 

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References

REFERENCES

Bachmann, E. R., Yun, X. and Brumfield, A. (2007). Limitations of attitude estimation algorithms for Inertial/Magnetic sensor modules. IEEE Robotics & Automation Magazine, 3(14), 76–87.Google Scholar
Barrau, A. and Bonnabel, S. (2013). Intrinsic filtering on Lie groups with applications to attitude estimation. IEEE Transactions on Automatic Control, 60(2), 436–449.Google Scholar
Calusdian, J., Yun, X. and Bachmann, E. (2011). Adaptive-gain complementary filter of inertial and magnetic data for orientation estimation, 2011 IEEE International Conference on Robotics and Automation, Shanghai, 1916–1922.Google Scholar
Cappello, F., Sabatini, R., Ramasamy, S. and Marino, M. (2015). Particle filter based multi-sensor data fusion techniques for RPAS navigation and guidance. Metrology for Aerospace, 395–400.Google Scholar
Carmi, A. and Oshman, Y. (2012). Adaptive Particle Filtering for Spacecraft Attitude Estimation from Vector Observations. Journal of Guidance, 32(1), 232–241.Google Scholar
Chang, H. K., Chan, G. P. and Jin, W. S. (2016). An Adaptive Complementary Kalman Filter Using Fuzzy Logic for a Hybrid Head Tracker System. IEEE Transactions on Instrumentation & Measurement, 65(9), 1–11.Google Scholar
Chang, R. H., Mu, X. D. and Shen, X. W. (2011). Attitude Estimation with Complementary Filter. Applied Mechanics & Materials, 44–47, 3781–3784.Google Scholar
Cheguini, M. and Ruiz, F. (2012). Real-time attitude estimation based on Gradient Descent algorithm. IEEE 4th Colombian Workshop on Circuits and Systems, 1–6.Google Scholar
Edwan, E., Zhang, J., Zhou, J. and Loffeld, O. (2011). Reduced DCM based attitude estimation using low-cost IMU and magnetometer triad. 2011 8th Workshop on Positioning, Navigation and Communication, Dresden, 1–6.Google Scholar
Euston, M., Coote, P., Mahony, R. and Kim, J. (2008). A complementary filter for attitude estimation of a fixed-wing UAV. IEEE/RSJ International Conference on Intelligent Robots and Systems. Nice, 340–345.Google Scholar
Grip, H. F., Fossen, T. I., Johansen, T. A. and Saberi, A. (2012). Attitude estimation using biased gyro and vector measurements with time-varying reference vectors. IEEE Transactions on Automatic Control, 57(5), 1332–1338.Google Scholar
Hide, C., Moore, T. and Smith, M. (2003). Adaptive Kalman Filtering for Low-cost INS/GPS. Journal of Navigation, 56(1), 143–152.Google Scholar
Hoflinger, F., Muller, J., Zhang, R., Reindl, LM and Burgard, W. (2013). A wireless micro inertial measurement unit (IMU). Instrumentation and Measurement, 62(9), 2583–2595.Google Scholar
Hyde, RA., Ketteringham, LP., Neild, SA and Jones, RS. (2008). Estimation of upper-limb orientation based on accelerometer and gyroscope measurements. IEEE transactions on bio-medical engineering, 55(2), 746–754.Google Scholar
Jategaonkar, R. V. (2015). Bounded-Variable Gauss-Newton Algorithm for Aircraft Parameter Estimation. Journal of Aircraft, 37(4), 742–744.Google Scholar
Ligorio, G. and Sabatini, A. (2015). A Novel Kalman Filter for Human Motion Tracking with an Inertial-based Dynamic Inclinometer. IEEE Transactions on Bio-Medical Engineering, 62(8), 2033–2043.Google Scholar
Liu, B and Tang, WS. (2006). Modern Control Theory. China Machine Press.Google Scholar
Liu, F., Li, J., Wang, H. and Liu, C. (2014). An improved quaternion Gauss–Newton algorithm for attitude determination using magnetometer and accelerometer. Chinese journal of Aeronautics, 27(4), 986–993.Google Scholar
Lou, L., Neal, M., Labrosse, F. and Cao, J. (2011). An approach to improving attitude estimation based on low-cost MEMS-IMU for mobile robot navigation. Towards Autonomous Robotic Systems, Sheffield, 378–379.Google Scholar
Madgwick, S. O. H, Harrison, A. J. L. and Vaidyanathan, R. (2011). Estimation of IMU and MARG orientation using a gradient descent algorithm, IEEE International Conference on Rehabilitation Robotics, Zurich, 1–7.Google Scholar
Mahony, R, Tarek, Hamel, Jean-Michel, Pflimlin. (2008). Nonlinear Complementary Filters on the Special Orthogonal Group. IEEE Transactions on Automatic Control, 53 (5), 1203–1217.Google Scholar
Marina, H. G. D., Pereda, F. J., Giron-Sierra, J. M. and Espinosa, F. (2012). UAV Attitude Estimation Using Unscented Kalman Filter and TRIAD. IEEE Transactions on Industrial Electronics, 59(11), 4465–4474.Google Scholar
Markley, F. L. and Sedlak, J. E. (2008). Kalman Filter for Spinning Spacecraft Attitude Estimation. Journal of Guidance Control & Dynamics, 31(6), 1750–1760.Google Scholar
Nowicki, M., Wietrzykowski, J. and Skrzypczynski, P. (2015). Simplicity or flexibility? Complementary Filter vs. EKF for orientation estimation on mobile devices. IEEE International Conference on Cybernetics, 166–171.Google Scholar
Tian, Y., Wei, H. and Tan, J. (2013). An Adaptive-Gain complementary filter for Real-Time human motion tracking with MARG sensors in Free-Living environments. Neural Systems and Rehabilitation Engineering, 21(2), 254–264.Google Scholar
Tseng, SP., Li, WL., Sheng, CY., Hsu, JW. and Chen, CS. (2011). Motion and attitude estimation using inertial measurements with complementary filter. 2011 8th Asian Control Conference, Kaohsiung, 863–868.Google Scholar
Wang, W. L. J. (2013). Effective Adaptive Kalman Filter for MEMS-IMU/Magnetometers Integrated Attitude and Heading Reference Systems. Journal of Navigation, 66(1), 99–113.Google Scholar
Widodo, R. B., Edayoshi, H. and Wada, C. (2014). Complementary filter for orientation estimation: Adaptive gain based on dynamic acceleration and its change. International Conference on Soft Computing and Intelligent Systems, Kitakyushu, 906–909.Google Scholar
Wu, J., Zhou, Z., Chen, J., Fourati, H and Li, R. (2016). Fast Complementary Filter for Attitude Estimation Using Low-Cost MARG Sensors. IEEE Sensors Journal, 16(18), 6997–7007.Google Scholar
Xue, Q., Leung, H., Wang, R., Liu, B and Wu, Y. (2015). Continuous Real-Time Measurement of Drilling Trajectory With New State-Space Models of Kalman Filter. IEEE Transactions on Instrumentation and Measurement, 65(1), 1–11.Google Scholar
Zhang, X., Xian, B., Zhao, B. and Zhang, Y. (2015). Autonomous flight control of a nano UAV helicopter in a GPS-denied environment using on-board vision. Industrial Electronics, 62(10), 6392–6403.Google Scholar