Abstract:

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×〖WTHZ〗R〖WTBX〗 n 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 RnResults 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.

Key words: Space situational awareness, Space surveillance, Gauss von Mises (GVM) distribution, Parameter estimation, Gauss von Mises (GVM) filtering