Comparación de Métodos de Filtrado Estadístico para Navegación de Satélites LEO con Señales GNSS

In this paper two methods of statistical filtering for satellite navigation in low earth orbit (LEO) with GNSS signals are compared. This problem is of a nonlinear nature, because both the state variables model and the output function are nonlinear. Hence, this is usually handled via extensions to the Kalman Filter. Two such variants of extended Kalman filter are compared, one that uses GNSS measurements directly and other that solves the nonlinearity states/measurements prior to the filtering. The latter can be thought of as a variant of the socalled Positioning Kalman Filtering (PKF), while maintaining the nonlinear state model.