Research On Orbit Prediction Algorithm Of Non Cooperative Target Based On Passive Measurement Information

With the development of human society, space resources have become another important resource for countries to compete with each other for a better development. In space tasks, it has been an important issue for researchers in different countries to predict the orbit of non-cooperative targets. During the process of non-cooperative targets tracking and navigation, using space-based devices have great advantages. In this paper, the tracking filter method for orbit prediction based on space-based measurements is discussed and the contents are organized as followings:Firstly, the research status of satellite orbit prediction technique and the tracking filter algorithm is summarized. Then the C-W and T-H equations are studied which are used as the basic module to describe the system. The applicability of the system equations is also analyzed. Then the observation equation is established according to the observation devices. After that, the observation conditions which should satisfy the requirements of the platform observation devices are discussed.Secondly, the pre-processing algorithm is established to provide an initial orbit data, which will improve the convergence speed and computational accuracy of the algorithm. Processed by the pre-processing algorithm, the observation data is used as the initial condition of the navigation algorithm. Based on the results above, the orbital prediction algorithm of non-cooperative targets is established using EKF. During the simulation, the influence of parameters in different working conditions is analyzed.To improve the accuracy of the algorithm, UKF and PF are adopted in the algorithm separately. Then the simulation is performed and the results are discussed and compared with each other.Finally, according to the particle degeneracy and impoverishment phenomenon of the standard particle filter, the firefly algorithm is adopted to modify the structure of particle filter. Through the simulation, the effectiveness and accuracy of the improved algorithm is better than that of the ordinary particle filter.