Strapdown Attitude Estimation Using IMU and Altimeter Integration for Maneuvering Vehicles

Document Type : Research Article

Authors

Abstract

In this paper, a new algorithm for attitude estimation in maneuvering flight, utilizing a combination of inertial measuring unit (IMU) and altimeter information is presented. Attitude estimation using a single IMU is possible only for near cruise flights, however for non-cruise flights, very large errors are obtained. In this paper, attitude estimation error is stabilized using an integrated IMU and altimeter system. The altimeter output being affected by gravity and the specific forces projected into the vertical plane bears insufficient information regarding the attitude states. Being a function of the roll and pitch angle, the specific forces will be in error, due to errors estimation of the attitude angles. Subsequently the vehicle vertical acceleration, speed and attitude will be inaccurate. In addition, due to a weak observability between the altitude measurements and the attitude angles to be estimated. For this reason and having a better estimate of the attitudes, the nonlinear attitude equations are converted into linear space, which will be beneficial for the estimation algorithm. Finally, simulation results using linear and unscented Kalman filters are curried out. A Monte Carlo simulation reveals that the newly suggested linear filter has a better performance in comparison with the non-linear unscented Kalman filter.

Keywords


[1] S.A. Whitmore, M. Fife, L. Brasher,” Development of strapdown attitude system for an ultrahigh altitude flight experiment”, NASA TM-4775, January 1997.
[2] G.M. Siouris, “Aerospace Avionics Systems: A Modern Synthesis”, Academic Press, New York,1993.
[3] B. Wie, “Space Vehicle Dynamics and Control”, AIAA Education Series”, AIAA Inc., USA,1998.
[4] Kayton, M. and W.R. Fried, “Avionics Navigation Systems”, John Wiley & Sons, Inc.,New York, 1969.
[5] Schuler, M: “Die Storung von Pendel- und kreiselapparaten durch die Beschleunigung des Fahrzeuges”, Physik. Z., vol. 24 July, 1923. [atranslation appears in G. R. Pitman, Jr. (ed),“Inertial Guidace,” john Wiley & Sons, Inc.,New York, 1962].
[6] C. J. Savant, R.C. Howard, C.B. Solloway, C.A. Savant, “Principles of Inertial Navigation”,McGraw-Hill, 1961.
[7] D. H. Titerton and J. L. Weston, “Strapdown Inertial Navigation Technology”, Peter Peregrinus Ltd., 1997.
[8] J.P. Gilmore, “Modular strapdown guidance unit with embedded micro processor”, J. Guid. Contr. 3 (1) (1980) 560–565
[9] M. Koifman, S.J. Merhav, “Autonomously aided strapdown attitude reference system”, J. Guid. Contr. Dyn. 14 (6) (1991) 1164–1172.
[10] S.K. Hong, “Compensation of nonlinear thermal bias drift of resonant rate sensor (RRS) using fuzzy logic, Sens”. Actuators A 73 (2–3) (1999)143–148
[11] Sung Kyung Hong, “Fuzzy logic based closed- loop strapdown attitude system for unmanned aerial vehicle (UAV)”, www.elsevier.com,accepted 4 June 2003.
[12] I. Madani, “An investigation into a reduced sensor fit for unmanned aircraft”, M.Sc. Thesis,Cranfield University, 1998.
[13] M. Wang, Y. Yang, R. R. Hatch, and Y. Zhang, “Adaptive Filter for a Miniature MEMS Based Attitude and Heading Reference System”, IEEE,April 26-29, 2004.
[14] L. Crassidis and E L. Markley, "Unscented Filtering for Spacecraft Attitude Estimation",Journal of Guidance. Conlrol, and Dynamics,vol. 26, no. 4, pp. 536542, July-August 2003.
[15] Guang-fu Ma, Xue-Yuan Jiang, “Unscented Kalman Filter for Spacecraft Attitude Estimation and Calibration using Magnetometer Measurements”, Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, 18-21 August 2005.
[16] E.J. Lefferts, F.L. Markley, M.D. Shuster, “Kalman Filtering for Spacecraft Attitude estimation”, J. Guidance, Vol.5, No.5, Sept-Oct 1982.
[17] J.F Guerrero Castellanos, S. Lesecq, N. Marchand, J. Delamare, “A Low-Cost Air Data attitude Heading Reference System for the Tourism Airplane Applications”.
[18] R. P. Kornfeld, R. J. Hansman and J.J. Deyst, “Single-Antenna GPS-Based Aircraft Attitude Determination”, Journal of The Institute of Navigation, Vol. 45, No.1, Spring 1998.
[19] W. F. Phillips, C. E. Hailey, "Review of Attitude Representations Used for Aircraft Kinematics",Journal of Aircraft, Vol. 38, No. 4, July-August,2001.
[20] D. Choukroun, I.Y. Bar-Itzhack, Y. Oshman, “A Novel Quaternion Kalman Filter”, 42th AIAA Guidance, Navigation, and Control Conference,Monterey, Aug. 2002.
[21] S. J. Julier and J. K. Uhlmann, "A General Method for Approximating Nonlinear Transformations of Probability Distributions,"Department of Engineering Science, University of Oxford, Oxford, OX1 3PI UK, Tech. Rep.,Nov 1996.
[22] E. A. Wan and R. van der Merwe, “Kalman Filtering and Neural Networks” Haykin, S. (Ed.),John Wiley & Sons, New York, 2001.
[23] Rudolph van der Merwe, “Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models”, Ph.D thesis, OGI School of Science & Engineering, Oregon Health & Science University, Portland, OR, April 2004.
[24] S. J. Julier and J. K. Uhlmann, “Unscented Filtering and Nonlinear Estimation” Proceedings of the IEEE, Vol. 92, No. 3, pp. 401–422, March 2004.
[25] Edgar Kraft, “A Quaternion-based Unscented Kalman Filter for Orientation Tracking”, in Proceedings of Fusion, Cairns, Australia, July 2003.
[26] H. Ghanbarpourasl, S.H. Pourtakdust, “UD Covariance Factorization for Unscented Kalman Filter using Sequential Measurements Update” Academy of Science, Engineering and technology, Vol. 25, Nov. 2007, ISSN 1307-6884