Adaptive Kalman Filtering for Trajectory Estimation of Hypersonic Glide Reentry Vehicles

WU Nan; CHEN Lei

doi:10.7527/S1000-6893.2013.0172

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2013 , Vol. 34 >Issue 8: 1960 - 1971

DOI: https://doi.org/10.7527/S1000-6893.2013.0172

Electronics and Control

Adaptive Kalman Filtering for Trajectory Estimation of Hypersonic Glide Reentry Vehicles

WU Nan ,
CHEN Lei

Expand

College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China

Received date: 2012-12-06

Revised date: 2013-03-18

Online published: 2013-03-25

Supported by

National Natural Science Foundation of China(41240031)

Fold

Abstract

The trajectory estimation of a hypersonic glide reentry vehicle (HGRV) usually uses traditional state augment methods, which have very large model simplification errors and the process noise variance of which are hard to build. In this study, based on the quantitative analysis results of the target movement property and the model simplification errors, the state equations are refined by using the spherical gravity model and fitting atmosphere model and considering the Coriolis force. The aerodynamic parameters are described using the first-order Markov process, and then the process noise variance is formulated as a function of the aerodynamic parameter variance and maneuvering time constant. Moreover, the time-varying aerodynamic parameter variance is obtained using the statistical result of the aerodynamic parameter estimate sequence based on the "fading memory" method, while the maneuvering time constant, as a target movement mode, is estimated along with the target base state by using a multi-model method of expected model augmentation. The simulation results show that the proposed algorithm can identify the time-varying variance of process noise effectively, and demonstrates better performance than traditional algorithms in the estimation precision of position, velocity and aerodynamics parameters, and has better engineering application value, robustness, and effectiveness-cost ratio.

Key words： hypersonic vehicles; target tracking; adaptive filtering; variable structure interacting multiple model; expected-mode augmentation

Cite this article

WU Nan , CHEN Lei . Adaptive Kalman Filtering for Trajectory Estimation of Hypersonic Glide Reentry Vehicles[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2013 , 34(8) : 1960 -1971 . DOI: 10.7527/S1000-6893.2013.0172

References

[1] Richie G. The common aero vehicle: space delivery system of the future. AIAA-1999-4435, 1999.
[2] Walker S H, Rodgers F. Falcon hypersonic technology overview. AIAA-2005-3253, 2005.
[3] Chang C B, Athans M, Whiting R H. On the state and parameter estimation for maneuvering reentry vehicles. IEEE Transactions on Automatic Control, 1977, 22(1): 99-105.
[4] Lee S C, Liu C Y. Trajectory estimation of reentry vehicle by use of on-line input estimator. Journal of Guidance, Control, and Dynamics, 1999, 22(6): 808-815.
[5] Hiroshi K, Shingo T, Yoshio K. Target tracking for maneuver in reentry vehicles using multiple maneuvering models. Electronics and Communications in Japan, 2000, 83(12): 84-92.
[6] Zhang S C, Hu G D. Target tracking for maneuvering reentry vehicles with interactive multiple model unscented Kalman filter. Acta Automatica Sinica, 2007, 33(11): 1221-1225. (in Chinese) 张树春, 胡广大. 跟踪机动再入飞行器的交互多模型 Unscented 卡尔曼滤波方法. 自动化学报, 2007, 33(11): 1221-1225.
[7] Liang Y Q, Han C Z, Sun Y J. Modeling and multiple-model estimation of invariable-structure semi-ballistic reentry vehicle. Acta Automatica Sinica, 2011, 37(6): 700-712. (in Chinese) 梁勇奇, 韩崇昭, 孙耀杰. 不变结构半弹道式再入飞行器的建模与多模型方法估计. 自动化学报, 2011, 37(6): 700-712.
[8] Li X R, Bar-Shalom Y. A recursive multiple model approach to noise identification. IEEE Transactions on Aerospace and Electronic Systems, 1994, 30(3): 671-684.
[9] Li X R, Bar-Shalom Y. Multiple-model estimation with variable structure. IEEE Transactions on Automatic Control, 1996, 41(4): 478-493.
[10] Li X R, Jilkov V P, Ru J F. Multiple-model estimation with variable structure. Part VI: expected-mode augmentation. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(3): 853-867.
[11] Li X R, Jilkov V P. A survey of maneuvering target tracking. Part 2: motion models of ballistic and space targets. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(1): 96-119.
[12] Jia P R, Chen K J, He L. Ballistic for long-range rocket. Changsha: National University of Defence Technology Press, 1993. (in Chinese) 贾沛然, 陈克俊, 何力. 远程火箭弹道学. 长沙: 国防科学技术大学出版社, 1993.
[13] Li X R, Jilkov V P. A survey of maneuvering target tracking: dynamic models. Proceedings of 2000 SPIE Conference on Signal and Data Processing of Small Targets, 2000, 4048: 212-235.
[14] Lawton J A, Jesionowski R J, Aarchan P. Comparison of four filtering options for a radar tracking problem. Journal of Guidance, Control, and Dynamics, 1998, 21(4): 618-623.
[15] Li X R, Zhao Z L, Li X B. General model-set design methods for multiple-model approach. IEEE Transactions on Automatic Control, 2005, 50(9): 1260-1276.
[16] Mehrotra K, Mahapatra P R. A jerk model for tracking highly maneuvering targets. IEEE Transactions on Aerospace and Electronic Systems, 1997, 33(4): 1094-1105.
[17] Li X R, Jilkov V P. Survey of maneuvering target tracking. Part V: multiple-model methods. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4): 1255-1321.
[18] Julier S J, Uhlmann J K, Durrant-Whyte H F. A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Transactions on Automatic Control, 2000, 45(3): 477-482.
[19] He Y, Xiu J J, Zhang J W, et al. Radar data processing with applications. Beijing: Publishing House of Electronics Industry, 2009: 70-80. (in Chinese) 何友, 修建娟, 张晶炜, 等. 雷达数据处理及应用. 北京: 电子工业出版社, 2009: 70-80.
[20] Phillips T H. A common aero vehicle (CAV) model, description, and employment guide. Schafer Corporation for AFRL and AFSPC, 2003.

Options

Outlines

模态框（Modal）标题

Abstract

Cite this article

References