Self-Calibration Estimation of Mars Vehicle in its Entry Phase

Abstract

There always are unknown inputs in the dynamic model of high-speed Mars vehicle during the Mars entry phase because of the atmospheric density, aerodynamic parameters, gust and sand storm, etc. Due to the effect of the unknown inputs, the traditional filtering methods may produce greater errors. This paper estimates the states of the Mars entry vehicle by using a new self-calibration extended Kalman filter, and successfully eliminates the effect of the unknown inputs. Numerical simulation shows that this self-calibration method may effectively improve navigation accuracy.

Keywords

Mars exploration / Mars entry / unknown input / state estimation / self-calibration extended Kalman filter

References

[1] Braun R D, Manning R M. Mars exploration entry, descent, and landing challenges[J]. Journal of Spacecraft and Rockets, 2007,44(2):310-323.
[2] 崔平远,于正湜,朱圣英.火星进入段自主导航技术研究现状与展望[J].宇航学报,2013,34(4):447-456. [Cui P Y,Yu Z S,Zhu S Y. Research progress and prospect of autonomous navigation techniques for Mars entry phase[J]. Journal of Astronautics, 2013,34(4):447-456.]
[3] Li S, Jiang X Q. Review and prospect of guidance and control for Mars atmospheric entry[J]. Progress in Aerospace Sciences, 2014(69):40-57.
[4] Gillijns S, De Moor B. Unbiased minimum-variance input and state estimation for linear discrete-time systems[J]. Automatica, 2007,43(1):111-116.
[5] Blanke M, Schrder J. Diagnosis and fault-tolerant control[M]. New York: Springer, 2003.
[6] Fonod R, Henry D, Charbonnel C, et al. A class of nonlinear unknown input observer for fault diagnosis: application to fault tolerant control of an autonomous spacecraft[C]//France: [s.n.], 2014.
[7] Schmidt S F. Application of state space methods to navigation problems[M]. New York: Academic Press, 1966(3):293-340.
[8] Li S, Peng Y M. Radio beacons/IMU integrated navigation for Mars entry[J]. Advances in Space Research, 2011,47(7):1265-1279.
[9] Woodbury D, Junkins J. On the consider Kalman filter[C]//Proceedings of the AIAA Guidance, Navigation, and Control Conference. Toronto, Canada: AIAA, 2001.
[10] Lou T S, Fu H M, Wang Z H, et al. Schmidt-Kalman filter for navigation biases mitigation during Mars entry[J]. Journal of Aerospace Engineering, 2015,28(4):11-18.
[11] Berman N, Shaked U. H_∞ nonlinear filtering[J]. International Journal of Robust and Nonlinear Control, 1996,6(4):281-296.
[12] Hsieh C S, Chen F C. Optimal solution of the two-stage Kalman estimator[J]. Automatic Control, IEEE Transactions on, 1999,44(1):194-199.
[13] Hsieh C S. Robust two-stage Kalman filters for systems with unknown inputs[J]. Automatic Control, IEEE Transactions on, 2000,45(12):2374-2378.
[14] Wu Y, Fu H, Xiao Q, et al. Extension of robust three-stage Kalman filter for state estimation during Mars entry[J]. Radar, Sonar & Navigation, IET, 2014,8(8):895-906.
[15] 傅惠民,吴云章,娄泰山,等.自校准Kalman滤波方法[J].航空动力学报,2014,29(6):1363-1368. [Fu H M,Wu Y Z,Lou T S, et al. Self-calibration Kalman filter method[J]. Journal of Aerospace Power, 2014,29(6):1363-1368.]
[16] 傅惠民,娄泰山,肖强,等.自校准扩展Kalman滤波方法[J].航空动力学报,2014,29(11):2710-2715. [Fu H M,Lou T S,Xiao Q, et al. Self-calibration extended Kalman filter method[J]. Journal of Aerospace Power, 2014,29(11):2710-2715.]
[17] Grewal M S, Andrews A P. Kalman filtering: Theory and practice using MATLAB[M]. New Jersey: John Wiley & Sons, Inc., 2008.
[18] Vinh N X, Busemann A, Culp R D. Hypersonic and planetary entry flight mechanics[R]. Michigan: NASA STI/Recon Technical Report A. 1980(81):16245.
[19] 彭玉明.新型火星EDL导航、制导与控制技术研究[D].南京:南京航空航天大学,2011. [Peng Y M. Innovative navigation guidance and control technology for Mars entry descent and landing[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2011.]
[20] F Levesque J, de Lafontaine J. Innovative navigation schemes for state and parameter estimation during mars entry[J]. Journal of Guidance, Control, and Dynamics, 2007,30(1):169-184.