Recursive filtering algorithm for space surveillance tracking applications

被引：0

作者：

Chen M.-Y. ^{[1
]}

Tian Y. ^{[1
]}

Wang H.-Y. ^{[1
]}

机构：

[1] School of Information Science and Engineering, Shenyang Ligong University, Shenyang

来源：

Yuhang Xuebao/Journal of Astronautics | 2016年 / 37卷 / 07期

关键词：

Gauss von Mises (GVM) distribution; Gauss von Mises (GVM) filtering; Parameter estimation; Space situational awareness; Space surveillance;

D O I：

10.3873/j.issn.1000-1328.2016.07.013

中图分类号：

学科分类号：

摘要：

In space surveillance tracking environment, in order to improve the estimation accuracy for the state of a space object which includes an angular variable, the Gauss von Mises (GVM) distribution defined on S×Rn is employed, a GVM parameter estimation method is proposed, the deterministic sampling algorithm for GVM distribution is improved, and finally the GVM recursive filtering algorithm is developed. The algorithm takes into consideration the intrinsic structure of the manifold, instead of adopting the traditional Gaussian distribution assumption which the state variable is defined on Rn. Results demonstrate that the proposed GVM filtering algorithm can estimate the posterior probability distribution of the state vector effectively, and more accurate results can be achieved compared to the traditional extended Kalman filter (EKF) especially for angular variable. © 2016, Editorial Dept. of JA. All right reserved.

引用

页码：862 / 868

页数：6

共 15 条

[1] Horwood J.T., Poore A.B., Orbital state uncertainty realism, Proceedings of the Advanced Maui Optical and Space Surveillance Technologies Conference, (2012)
[2] Liu Y.-L., Feng X.-X., Lu C.-G., Et al., A fading quadrature Kalman filter for nonlinear systems, Journal of Astronautics, 34, 10, pp. 1370-1377, (2013)
[3] Horwood J.T., Aragon N.D., Poore A.B., Gaussian sum filters for space surveillance: theory and simulations, Journal of Guidance, Control, and Dynamics, 34, 6, pp. 1839-1851, (2011)
[4] Ristic B., Arulampalam S., Gordon N., Beyond the Kalman Filter: Particle Filters for Tracking Applications, (2004)
[5] Montenbruck O., Gill E., Satellite Orbits: Models, Methods, and Applications, (2000)
[6] Mardia K.V., Sutton T.W., A model for cylindrical variables with applications, Journal of the Royal Statistical Society, 40, Series B, pp. 229-233, (1978)
[7] Mardia K.V., Hughes G., Taylor C.C., Et al., A multivariate von Mises distribution with applications to bioinformatics, Canadian Journal of Statistics, 36, 1, pp. 99-109, (2008)
[8] Kurz G., Gilitschenski I., Hanebeck U.D., The partially wrapped normal distribution for SE(2) estimation, 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI), (2014)
[9] Stienne G., Reboul S., Azmani M., Et al., A multi-temporal multi-sensor circular fusion filter, Information Fusion, 18, 7, pp. 86-100, (2014)
[10] Glover J., Kaelbling L.P., Tracking 3-D rotations with the quaternion bingham filter, (2013)

← 1 2 →