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Estimation of satellite three-axis attitude using only one sensor data presents an interesting estimation problem. A flexible and mathematically effective filter for solving the satellite three-axis attitude estimation problem using two-axis magnetometer would be a challenging option for space missions which are suffering from other attitude sensors failure. Mostly, magnetometers are employed with other attitude sensors to resolve attitude estimation. However, by designing a computationally efficient discrete Kalman filter, full attitude estimation can profit by only two-axis magnetometer observations. The method suggested solves the problem of satellite attitude estimation using linear Kalman filter (LKF). Firstly, all models are generated and then the designed scenario is developed and evaluated with simulation results. The filter can achieve 10e-3 degree attitude accuracy or better on all three axes.
Czasopismo
Rocznik
Tom
Strony
577--590
Opis fizyczny
Bibliogr. 24 poz., rys., tab., wykr., wzory
Twórcy
autor
- National University of Malaysia (UKM), Institute of Space Science, 43600 Bangi, Malaysia
autor
- National University of Malaysia (UKM), Institute of Space Science, 43600 Bangi, Malaysia
autor
- National University of Malaysia (UKM), Institute of Space Science, 43600 Bangi, Malaysia
Bibliografia
- [1] Habib, T.M.A. (2013). A comparative study of spacecraft attitude determination and estimation algorithms (a cost-benefit approach). Aerospace Science and Technology, 26(1), 211−215.
- [2] Boardman, T.J. (1979). Prediction and Improved Estimation in Linear Models. Technomet., 21(4), 582–582.
- [3] Lefferts, E.J., Markley, F.L., Shuster, M.D. (1982). Kalman filtering for spacecraft attitude estimation. Journal of Guidance, Control, and Dynamics, 5(5), 417–429.
- [4] Wertz, J.R. (1978). Spacecraft attitude determination and control. Springer Scien. & Busin. Media. 73, 858.
- [5] Wahba, G. (1965). A least squares estimate of satellite attitude. SIAM review, 7(3), 409–409.
- [6] Shuster, M.D. (2006). The quest for better attitudes. Journal of the Astronautical Sciences, 54(3–4), 657–683.
- [7] Markley, F.L. (1988). Attitude determination using vector observations and the singular value decomposition. Journal of the Astronautical Sciences, 36(3), 245−258.
- [8] Santoni, F., Bolotti, F. (200 0). Attitude determination of small spinning spacecraft using three axis magnetometer and solar panels data. Aerospace Conference Proc., 2000 IEEE, 7,. 127−133.
- [9] Markley, F.L. (1993). Attitude determination using vector observations. A fast optimal matrix algorithm. 41, 261–80.
- [10] Faragher, R. (2012). Understanding the basis of the Kalman filter via a simple and intuitive derivation. IEEE Signal processing magazine, 29(5), 128−132.
- [11] Gelb, A. (ed.). (1974). Applied optimal estimation. MIT press, 374.
- [12] Ma, G.F., Jiang, X.Y. (2005). Unscented Kalman filter for spacecraft attitude estimation and calibration using magnetometer measurements. Machine Learning and Cybernetics. Proc. Intern. Conf., 1, 506–511.
- [13] Psiaki, M.L., Martel, F., Pal, P.K. (1990). Three-axis attitude determination via Kalman filtering of magnetometer data. Journal of Guidance, Control and Dynamics, 13(3), 506–514.
- [14] De Ruiter, A. (2010). A simple suboptimal Kalman filter implementation for a gyro-corrected satellite attitude determination system. Proc. Inst. Mech. Eng., Part G: Journal Aerospace Engineering, 224(7), 787–802.
- [15] Sidi, M.J. (1997). Spacecraft dynamics and control: a practical engineering approach. Cambridge University Press.
- [16] Bolandi, H., Abedi, M., Nasrollahi, S. (2013). Design of an analytical fault tolerant attitude determination system using Euler angles and rotation matrices for a three-axis satellite. Proc. Inst. Mech. Eng., Part G: Journal of Aerospace Engineering, 0954410013479068.
- [17] Brown, R, Hwang, P.Y. (1996). Introduction to Random Signals and Applied Kalman Filtering with Matlab Exercises and Solutions. Wiley.
- [18] Anderson, J.L., Anderson, S.L. (1999). A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Monthly Weather Review, 127(12), 2741−2758.
- [19] Van der Merwe, R., Wan, E. (2003). Gaussian mixture sigma-point particle filters for sequential probabilistic inference in dynamic state-space models. Acoustics, Speech, and Signal Processing. (ICASSP’03), 6, VI–701.
- [20] Julier, S.J., Uhlmann, J.K. (2004). Unscented filtering and nonlinear estimation. Proc. IEEE, 92(3), 401–422.
- [21] Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. Signal Processing. IEEE Transactions on, 50(2), 174−188.
- [22] Shou, H.N., Lin, C.T. (2010, May). Micro-satellite attitude determination: using Kalman filtering of magnetometer data approach. Computer Communication Control and Automation, 195−198.
- [23] World Magnetic Model, NGIA, http://www.ngdc.noaa.gov/geomag /WMM/DoDWMM.shtml
- [24] Ogata, K. (1997). Modern Control Engineering. 299−231.
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-5a48436b-a7b4-4002-bc80-9f317144c3b3
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