Based on Quantum Particle Swarm Optimization and Unscented Kalman Filter Orbit State Prediction
Gaofeng Li, Lei Wang, Yi Tan · 2012
An orbit state prediction framework is developed with the combination of unscented Kalman filter (UKF) and quantum particle swarm optimization (QPSO). The accurate state prediction decides the performance of spacecraft guidance, navigation and control. Because of the nonlinearity of the state and measurement equation, UKF is gradually applied to nonlinear state estimation and prediction, especially for autonomous orbit spacecraft. Since QPSO is developed to search solution spaces with quantum well conception, it is appropriate to deal with the orbit optimization. We propose a method utilizing quantum particles in order to optimize desired parameter feature of UKF. It conduces to improve the performance of sigma samples of UKF, and obtain efficiently predicting results. The effectiveness is demonstrated with an orbit dynamics simulation in this paper.