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All issues Volume 150 (2018) MATEC Web of Conferences, 150 (2018) 06010 References
Open Access
Issue
MATEC Web of Conferences
Volume 150, 2018
Malaysia Technical Universities Conference on Engineering and Technology (MUCET 2017)
Article Number 06010
Number of page(s) 6
Section Information & Communication Technology (ICT), Science (SCI) & Mathematics (SM)
DOI https://doi.org/10.1051/matecconf/201815006010
Published online 23 February 2018
  1. J.L. Crassidis, F.L. Markley: An MME-based Attitude Estimator using Vector Observations. NASA Conf. Publ. pp. 137 (1995) [Google Scholar]
  2. J.A. Christian, E.G. Lightsey: The Sequential Optimal Attitude Recursion Filter.J. Guid. Control. Dyn. pp. 1787–1800 (2010) [Google Scholar]
  3. H.E. Emara-Shabaik: Spacecraft Spin Axis Attitude. IEEE Trans. Aerosp. Electron. Syst. vol. 28, no. 2, pp. 529-534 (1992) [CrossRef] [Google Scholar]
  4. P. Appel: Attitude Estimation from Magnetometer and Earth-Albedo-Corrected Coarse Sun Sensor Measurements.Acta Astronaut. vol. 56, no. 2, pp. 115–126 (2005) [Google Scholar]
  5. B. Liu, Z. Chen, X. Liu, F. Yang: An Efficient Nonlinear Filter for Spacecraft Attitude Estimation. Int. J. Aerosp. Eng. vol. 2014, pp. 1–11 (2014) [CrossRef] [Google Scholar]
  6. M.N. Filipski & R. Varatharajoo: Evaluation of a Spacecraft Attitude and Rate Estimation Algorithm. Aircr. Eng. Aerosp. Technol. vol. 82, no. 3, pp. 184–193 (2010) [Google Scholar]
  7. M. Fadly, O. Sidek, A. Said, H. Djojodihardjo, A. Ain: Deterministic and Recursive Approach in Attitude Determination for InnoSAT. TELKOMNIKA, vol. 9, no. 3, pp. 583–594 (2011) [CrossRef] [Google Scholar]
  8. Y.J. Cheon, J.H. Kim: Unscented Filtering in a Unit Quaternion Space for Spacecraft Attitude Estimation.In 2007 IEEE Int. Symp. Ind. Electron. pp. 66–71 (2007) [Google Scholar]
  9. M.C. Vandyke, J.L. Schwartz, C.D. Hall: Unscented Kalman Filtering for Spacecraft Attitude State and Parameter Estimation. Department of Aerospace & Ocean Engineering, Virginia Polytechnic Institute & State University, pp. 1–13 (2004) [Google Scholar]
  10. J.H. Bae, Y.D. Kim: Attitude Estimation for Satellite Fault Tolerant System using Federated Unscented Kalman Filter. Int. J. Aeronaut. Sp. Sci., vol. 11, no. 2, pp. 80–86 (2010) [Google Scholar]
  11. Y.Liu, X. Jiang, G. Ma: Marginalized Particle Filter for Spacecraft Attitude Estimation from Vector Measurements.J. Control Theory Appl., vol. 5, no. 1, pp. 60–66 (2007) [Google Scholar]
  12. Y. Yafei, L. Jianguo: Particle Filtering for Gyroless Attitude/Angular Rate Estimation Algorithm. In 2010 First Int. Conf. Pervasive Comput. Signal Process. pp. 1188–1191 (2010) [Google Scholar]
  13. Y. Cheng, J.L. Crassidis: Particle Filtering for Sequential Spacecraft Attitude Estimation. In Proc. of the 2004 AIAA Guidance, Navigation, and Control Conference, p. 5337 (2004) [Google Scholar]
  14. J.L. Crassidis, F.L. Markley, E.G. Lightsey, E. Ketchum: Predictive Attitude Estimation using Global Positioning System Signals. In NASA Conference Publication, pp. 1–14 (1997) [Google Scholar]
  15. J.L. Crassidis, F.L. Markley: Predictive Filtering for Attitude Estimation Without Rate Sensors. J. Guid. Control. Dyn., vol. 20, no. 3, pp. 522–527 (1997) [Google Scholar]
  16. M.D. Shuster: A survey of Attitude Representation. J. Astronaut. Sciense, vol. 41, no. 4, pp. 493–517 (1993) [Google Scholar]
  17. M.J. Sidi: Spacecraft Dynamics and Control: A Practical Engineering Approach. Cambridge University Press, Cambridge (1997) [Google Scholar]
  18. J.L. Crassidis, J.L. Junkins: Optimal Estimation of Dynamic Systems. CRC Press, Boca Raton (2004) [Google Scholar]
  19. M.M. Aly, H.A. Abdel Fatah, A. Bahgat: Nonlinear Observers for Spacecraft Attitude Estimation in Case of Yaw Angle Measurement Absence. Int. J. Control. Autom. Syst., vol. 8, no. 5, pp. 1018–1028 (2010) [Google Scholar]
  20. N.J. Gordon, D.J. Salmond, A.F.M. Smith: Novel Approach to Nonlinear/Non-Gaussian Bayesian State Estimation. IEE Proc. F Radar Signal Process., vol. 140, no. 2, pp. 107–113 (1993) [Google Scholar]
  21. B. Ristic, M.S. Arulampalam, N. Gordon: Beyond the Kalman filter: Particle filters for Tracking Applications.Artech House, Boston (2004) [Google Scholar]
  22. M.S. Arulampalam, S. Maskell, N. Gordon, T. Clapp: A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Trans. Signal Process, vol. 50, no. 2, pp. 174–188 (2002) [Google Scholar]

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