Orbit Propagation and Determination using Genetic Algorithms

In order to operate effectively, the orbital parameters of the satellite are essential, to estimate satellite position over the previous and in the near future. For the planning of satellite missions and for linking satellite location data to accurate geographical locations, such knowledge is essential. However, determining satellite orbits poses a significant challenge for small to medium satellite operators lacking the necessary tracking infrastructure. These organizations often rely on third-party services like Celestrak, which offer orbital information but may not provide it with the required frequency. Moreover, in the initial stages of a mission, especially when multiple satellites are launched together, it becomes challenging to attribute specific orbital parameters to individual satellites within tshe group. This ambiguity hampers mission planning and monitoring efforts. To address these challenges, this research presents an approach to tackle the problem of orbital parameter determination by leveraging Global Positioning System (GPS) data, and artificial intelligence, specifically genetic algorithms, to enhance the accuracy and efficiency of orbit determination algorithms and orbit propagation techniques, thus enabling small and medium satellite operators to reliably obtain vital orbital parameters.