Document Type : Original Article


1 University of Tehran

2 university of Tehran


In this study, Adaptive Network-Based Fuzzy Inference System (ANFIS) is presented with sensor data fusion approach to estimate satellite attitude. The active sensors are sun and earth sensors. Satellite attitude dynamic, including attitude quaternion and angular velocities are estimated simultaneously utilizing the measured values by the sensors. The Extended Kalman Filter (EKF) is employed to verify and evaluate the efficiency of the presented method. Additionally, the neural networks with Radial Basis Function (RBF) and Multi-Layer Perceptron (MLP) are also designed to prove the superiority of the proposed ANFIS network among the smart methods of sensor data fusion for satellite attitude estimation. Root Mean Square Error (RMSE) as a numerical criterion and graphical analysis of residues are utilized to evaluate the simulation results. The simulations confirm that the obtained estimations from ANFIS network have more accuracy in modeling of nonlinear complex systems compared to EKF, MLP and RBF networks. In general, using intelligent data fusion, especially ANFIS, reduces attitude estimation error and time in comparison to the classical EKF method.


Main Subjects

1] Sidi, Marcel J. Spacecraft dynamics and control: a practical engineering approach. Vol. 7. Cambridge university press, 1997.
2) Fakoor, M., Nikpay, S., & Kalhor, A. (2021). On the ability of sliding mode and LQR controllers optimized with PSO in attitude control of a flexible 4-DOF satellite with time-varying payload. Advances in Space Research, 67(1), 334-349.
3) Guo, N., & Zhan, W. (2020). Influence of different data fusion methods on the accuracy of three-dimensional displacements field. Advances in Space Research, 65(6), 1580-1590.
4) Zhang, C., Dai, M. Z., Wu, J., Xiao, B., Li, B., & Wang, M. (2021). Neural-networks and event-based fault-tolerant control for spacecraft attitude stabilization. Aerospace Science and Technology, 114, 106746.
5) Cheng, L., Wang, Z., Jiang, F., & Li, J. (2021). Adaptive neural network control of nonlinear systems with unknown dynamics. Advances in Space Research, 67(3), 1114-1123.
6) Pittelkau, M. E. "Kalman filtering for spacecraft system alignment calibration", Journal of Guidance, Control and Dynamics, Vol. 24, No. 6, pp. 1187-1195, July 2002.
7) Hajiyev, Chingiz, Demet Çilden, and Yevgeny Somov. "Integrated SVD/EKF for Small Satellite Attitude Determination and Rate Gyro Bias Estimation." IFAC-PapersOnLine 48, no. 9 (2015): 233-238.
8) Xing, Y., Zhang, S., Zhang, J., & Cao, X. (2011). Robust-extended Kalman filter for small satellite attitude estimation in the presence of measurement uncertainties and faults. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering,
9) Cao, L., Yang, W., Li, H., Zhang, Z., & Shi, J. (2017). Robust double gain unscented Kalman filter for small satellite attitude estimation. Advances in Space Research,
10) Hyun-Sam Myung, Ki-Lyuk Yong and Hyo-Choong Bang (2011). Unscented Kalman Filtering for Hybrid Estimation of Spacecraft Attitude Dynamics and Rate Sensor Alignment, Advances in Spacecraft Technologies, Dr Jason Hall (Ed.), ISBN: 978-953-307-551-8, InTech.
11) T. Ejiang, F. R. Chang, L. S. Wang "Data Fusion of Three Attitude Sensors" SICE, July 25-27, 2001, Nagoya.
12) Pham, Minh Duc, Kay Soon Low, Shu Ting Goh, and Shoushun Chen. "Gain-scheduled extended kalman filter for nanosatellite attitude determination system." Aerospace and Electronic Systems, IEEE Transactions on 51, no. 2 (2015): 1017-1028.
13) Souza, A. L., Ishihara, J. Y., Ferreira, H. C., Borges, R. A., & Borges, G. A. (2016). Antenna pointing system for satellite tracking based on Kalman filtering and model predictive control techniques. Advances in Space Research, 58(11), 2328-2340. 14) Choi, E. J., Yoon, J. C., Lee, B. S., Park, S. Y., & Choi, K. H. (2010). Onboard orbit determination using GPS observations based on the unscented Kalman filter. Advances in Space Research, 46(11), 1440-1450.
15) Xiong, Kai, Tang Liang, and Lei Yongjun. "Multiple model Kalman filter for attitude determination of precision pointing spacecraft." Acta Astronautica 68, no. 7 (2011): 843-852.
16) Jiang, C., & Hu, Q. (2020). Constrained Kalman filter for uncooperative spacecraft estimation by stereovision. Aerospace Science and Technology, 106, 106133.
17) Cemenska, Joshua. "Sensor Modelling and Kalman Filtering Applied to Satellite Attitude Determination." PhD diss., University of California at Berkeley, 2004.
18) Garcia, R. V., Pardal, P. C. P. M., Kuga, H. K., & Zanardi, M. C. (2019). Nonlinear filtering for sequential spacecraft attitude estimation with real data: Cubature Kalman Filter, Unscented Kalman Filter and Extended Kalman Filter. Advances in Space Research, 63(2), 1038-1050.
19) Markley, F. Landis, and Joseph E. Sedlak. "Kalman filter for spinning spacecraft attitude estimation." Journal of guidance, control, and dynamics 31, no. 6 (2008): 1750-1760.
20) Mirmomeni, M., K. Rahmani, and C. Lucas. "Spacecraft attitude estimation with the aid of Locally Linear Neurofuzzy models and multi sensor data fusion approaches." Autonomous Robots and Agents, 2009. ICARA 2009. 4th International Conference on. IEEE, 2009.
21) Yue, X. K., & Yuan, J. P. (2006). Neural network-based GPS/INS integrated system for spacecraft attitude determination. Chinese Journal of Aeronautics, 19(3), 233-238.
22) Hyunsam, M., Ki-Lyuk, Y., and Hyochoong, B., “Hybrid Estimation of Spacecraft Attitude Dynamics and Rate Sensor Alignment Parameters”, International Conference on Control, Automation and Systems, 2007.
23) Welch, Greg, and Gary Bishop. "An introduction to the kalman filter." Proceedings of the Siggraph Course, Los Angeles (2001).
24) Nelles, Oliver. Nonlinear system identification: from classical approaches to neural networks and fuzzy models. Springer Science & Business Media, 2001.
25) Qiuming Zhu, Yao Cai, Luzheng Liu, “A global learning algorithm for a RBF network, Neural Networks”, Volume 12, Issue 3, April 1999, Pages 527-540, ISSN 0893-6080, 10.1016/S0893-6080(98)00146-4.
26) Jang, JSR, ANFIS: Adaptive-network-based fuzzy inference system. Systems, Man and Cybernetics. 1993. 23.3:665-685.