Filtering Techniques for Estimating the Angular Motion Using All-Accelerometers

Abstract:

Linear accelerometers can be used to retrieve the angular motion of the body they are attached to if their measurements are handled in an appropriate way. In this paper, an 18-accelerometer configuration is used to facilitate angular motion determination. Various measurement models are investigated where each model utilizes different accelerometers’ measurements. These models are used to build linear and nonlinear filters. The filters performances are evaluated and the associated sign ambiguity problem is solved. It is shown that a norm-constrained Kalman filters bank will give the best results. This work can be used within inertial navigation systems as a replacement of the gyroscopes.

• Periodical: Applied Mechanics and Materials (Volume 829)
• Pages: 103-109
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.829.103
• Online since: March 2016
• Authors: Yazan M. Al-Rawashdeh*, Moustafa Elshafei
• Keywords: All-Accelerometers, Angular Motion Determination, Gyro-Free, Inertial Measurement Unit, Non/Linear Filters, Sign Ambiguity

References

[1] C. Tsung-Lin, Design and analysis of a fault-tolerant coplanar gyro-free inertial measurement unit, Journal of Microelectromechanical Systems 17. 1 (2008) 201-212.

DOI: 10.1109/jmems.2007.911372

[2] L. Pei-Chun, et al, Design and implementation of a nine-axis inertial measurement unit, IEEE/ASME Transactions on Mechatronics 17. 4 (2012) 657-668.

DOI: 10.1109/tmech.2011.2111378

[3] H. Chi-Wei, and Pei-Chun Lin, Design and implementation of a 12-axis accelerometer suite, IEEE/RSJ International Conference onIntelligent Robots and Systems (2009).

DOI: 10.1109/iros.2009.5353892

[4] P. Kourosh, T. Lasky, and B. Ravani, Design and implementation of a mechatronic, all-accelerometer inertial measurement unit, IEEE/ASME Transactions on Mechatronics 12. 6 (2007) 640-650.

DOI: 10.1109/tmech.2007.910080

[5] S. Alfred R., A. Grammatikos, and K. A. Fegley, Measuring rotational motion with linear accelerometers, IEEE transactions on aerospace and electronic systems 3. AES-3 (1967) 465-472.

DOI: 10.1109/taes.1967.5408811

[6] A. F. Mohammad, and M. Elshafei, U.S. Patent 8, 260, 477. (2012).

[7] Y. M. Al-Rawashdeh, M. Elshafei, and A. F. Mohammad, In-Flight Estimation of Center of Gravity Position Using All-Accelerometers, Sensors 14. 9 (2014) 17567-17585.

DOI: 10.3390/s140917567

[8] S. Dan, Optimal state estimation: Kalman, H infinity, and nonlinear approaches, John Wiley & Sons, (2006).

[9] Z. Renato, et al, Norm-constrained Kalman filtering, Journal of guidance, control, and dynamics 32. 5 (2009) 1458-1465.

[10] H. Paul J., and R. H. Bishop, Navigation algorithms and observability analysis for formation flying missions, Journal of guidance, control, and dynamics 32. 4 (2009) 1218-1231.

DOI: 10.2514/1.41288

[11] B. Vibhor L., et al, Stochastic observability test for discrete-time Kalman filters, Journal of Guidance, Control, and Dynamics 32. 4 (2009) 1356-1370.

DOI: 10.2514/1.38128

[12] E. Ezzaldeen, S. Knedlik, and O. Loffeld, Constrained angular motion estimation in a gyro-free IMU, IEEE Transactions on Aerospace and Electronic Systems 47. 1 (2011) 596-610.

DOI: 10.1109/taes.2011.5705694

[13] E. Ezzaldeen, S. Knedlik, and O. Loffeld, Angular Motion Estimation Using Dynamic Models in a Gyro-Free Inertial Measurement Unit, Sensors 12. 5 (2012) 5310-5327.

DOI: 10.3390/s120505310

[14] W. Qingya, et al, Angular velocity estimation based on adaptive simplified spherical simplex unscented Kalman filter in GFSINS, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 228. 8 (2014).

DOI: 10.1177/0954410013492255