Abstract
This paper focus on Nano satellite attitudes (Roll angles, Pitch angles, and Yaw angles) errors estimation using Kalman Filters. The filter predicts the future estimates from on board sensors such as IMU and magnetometer. The Kalman algorithms implemented with low-cost sensor using MATLAB/SIMULINK environment. The NPSAT-1 Nano satellite attitudes estimations was performed. The Aerodynamic and solar disturbances torque considered for the simulations. The satellite on-board sensors, IMU and Magnetometer into fuze the data with low earth orbit (LEO). NPSAT-1analysis of magnetometers data from reference LEO is (0–5000) Seconds in the orbit. The process and measurement error covariance considered with six state matrices (3 angular angles, 3 angular rates). The Nano satellite Kalman algorithm results accurately estimated the attitudes angles (Roll, Yaw, Pitch) with considered inertia of the model. Finding pointing accuracy of satellite 0.1° from the final value theorem vehicle steady states.
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References
Grewal, M. S., and A.P. Andrews. 2018. Kalman Filtering: Theory and Practice Using MATLAB, 3rd ed. Wiley.
Kushner, H.J. 1967. Dynamical Equations for Optimum Non-linear Filtering. Journal of Differential Equations 3: 179–190.
Julier, S.J., J. K. Uhlmann, and H. F. Durrant-Whyte. 1995. A New Approach for Filtering Nonlinear Systems. In Proceedings of the American Control Conference, 1628–1632, Seattle, Washington.
Wu, Y., D. Hu, M. Wu, and X. Hu. 2005. Unscented Kalman Filtering for Additive Noise Case: Augmented vs. Non-augmented. In American Control Conference.
Sorenson, H.W. (ed.). 1985. Kalman Filtering: Theory and Application. IEEE Press.
Markley, F.L. 2003. Attitude Error Representation. Journal of Guidance, Control and Dynamics 26 (2): 311–317.
Steyn, W.H. Magnetic Attitude Determination and Control for Low Earth Orbiting Small Satellites. Department of Electrical and Electronic Engineering, University of Stellenbosch, PO Box 1 Matieland 7602.
Vitalich, John. 2003. Design and Simulation of a Three-Axis Stabilized Satellite and Kalman Filter Rate Estimator. Master Thesis, Electrical Engineering, Naval Postgraduate School, Monterey, California.
Grewal, Andrews. (2001) Kalman Filtering Theory Using MATLAB.Wiley.
Bibby, J., and H. Toutenburg. 1977. Prediction and Improved Estimation in Linear Models. New York: Wiley.
Lefferts, E.J., F.L. Markey, M. D. Shuster. 1982. Kalman Filtering for Spacecraft Attitude Estimation. Journal of Guidance, Control and Dynamics, 5(5):417–429.
Sorenson, H.W., and A.R. Stubberud. 1968. Non-linear Filtering by Approximation of a Posteriori Density. International Journal of Control 8 (1): 33–51.
Julier, S.J., and J.K. Uhlmann. 2004. Unscented filtering and nonlinear estimation. Proceedings of the IEEE 92 (3): 401–422.
Crassidis, J.L., F.L. Markley, and Y. Cheng. 2007. Survey of Nonlinear Attitude Estimation Methods. Journal of Guidance, Control, and Dynamics, 30(1).
Muhammad Shamsul, K.A., and R. Radzuan. A Study of Perturbation Effect on Satellite Orbit using Cowell’s Method. School of Aerospace Engineering, Engineering Campus University Science Malaysia 14300 Nibong Tebal Penang, Malaysia.
Zagorski, Pawel. 2012. Modeling Disturbances Influencing an Earth-Orbiting Satellite. Journal of Pomiary Automatyka Robotyka AGH University of Science and Technology 16(5):98–103, 2012.
Singh, S., and W. Yim. (1995). Dynamic Feedback Linearization and Large Pitch Attitude Control of Satellite Using Solar Radiation Pressure. In Proceedings of the 1995 American Control Conference, vol. 5, 3131–3135.
Gordon, N.J., D.J. Salmond, and A.F.M. Smith. 1993. Novel Approach to Nonlinear/Non-Gaussian Bayesian State Estimation. IEEE Proceedings-F 140 (2): 107–113.
Sakoda, D., and J.A. Horning. 2002. Overview of the NPS Spacecraft Architecture and Technology Demonstration Satellite, NPSAT1. Naval Postgraduate School, Monterey, Tech. Rep. http://www.digitalcommons.usu.edu/cgi/view, http://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=1896&context=smallsat.
Bak, T. 2002. Spacecraft Attitude Determination–A Magnetometer Approach. Ph.D. Thesis. Aalborg University, Denmark, 2002.
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Raja, M., Guven, U. (2020). Nano Satellite Attitudes Error Estimation Using Magnetometer Data with Utilization of Kalman Filter. In: Mehta, A., Rawat, A., Chauhan, P. (eds) Recent Advances in Communication Infrastructure. Lecture Notes in Electrical Engineering, vol 618. Springer, Singapore. https://doi.org/10.1007/978-981-15-0974-2_1
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DOI: https://doi.org/10.1007/978-981-15-0974-2_1
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