A Secondary Particle Filter Photometric Data Inversion Method of Space Object Characteristics

Abstract

A secondary particle filter (SPF) inversion method for geostationary space object characteristics based on ground photometric data is presented. The method combines the estimation results of the standard particle filter (PF) algorithm and the resampling algorithm of the particle generation process. SPF first generates N particles according to the standard PF process, and performs the standard PF without resampling. Particle weight is an important indicator to determine the closeness of particles to the real state. With the progress of PF, the weight of particles closer to the real state will gradually increase. SPF takes the particle weight value as an important basis to judge the closeness of particles to the real state. By setting a threshold, the particles closest to the real state are screened out and roughened. The SPF method in this paper uses a particle filter twice and it is a new particle filter method. The first particle filter identifies particles near the real state. Before the second particle filter, it is equivalent to the actual state distribution of the system is known, so that the distribution of initial particles can be set more efficiently and effectively, and the number of particles close to the real state of the system can be increased. Experiment results show that the estimation error and the RMSE of the inversion error of SPF are less than PF, which not only shows that the inversion result based on SPF is better than the inversion result based on PF, but also proves the effectiveness of the inversion method based on SPF.