چكيده به لاتين
In satellite relative navigation, which is known as one of the most important space applications,
the dynamic model equations and systems have many nonlinear parameters, so nonlinear
Kalman filters have been developed to replace the Extended Kalman filter. In this study, a
comparison was made between Kalman Cubature and Unscend filters. In this comparison, the
better performance of the Cubature filter is shown compared to the Unscend filter. In this study,
the interface between STK and MATLAB was used to obtain the actual and correct values of
the dynamic model. Since the noise in the system is not white noise, the color noise is modeled
instead of white noise. Other parameters are also correlated to make the simulation closer to
the real state. These assumptions increase the estimation error that can be achieved by
providing a robust Cubature filter to improve the accuracy of the system in the presence of
nonlinear relationships and color noise. Also, with optimizing the presented filter by using
particle swarm method, result will be improved and errors be reduced.
