Performance of Optimized Scheduled Follow-up Observations for geosynchronous Space Objects Using different Genetic Algorithms

The importance of protecting the geosynchronous region from space debris requires continuous monitoring in order to support collision avoidance operations. Accurate orbit information is prerequisite to avoid manoeuvres which might shorten the mission time. To gain this information, follow-up observations are necessary to maintain the accuracy of ephemeris data within certain limits for each catalogued object. To perform these observations in the most efficient way, optimized scheduling is a key element. In this paper the performance of two optimal scheduling algorithms is compared for an optical telescope network. Both algorithms are based on genetic algorithms and have been utilized to provide optimal solutions for catalogue maintenance. The single-objective algorithm uses the expected information content of a new observation as optimization parameter. The multi-objective algorithm is based on the successful Non-dominated Sorting Genetic Algorithm II (NSGA-II) and uses as further optimization parameter the detection probability given by the pre-estimated magnitude of the object. Both algorithms are introduced in detail and it is shown that the information content utilizing the orbit’s covariance and the information gain in an expected update is an useful optimization measure. Since the information content of a follow-up observation depends also on the observation time and oscillates slightly during the night similar information gain values might be reached at different observation epochs. It is demonstrated that an optimized phase angle might not reduce the information content of a follow-up observation substantially. To prove the concept, data of a simulated object catalogue is used to compare the effectiveness of the scheduling algorithms. Finally, first results of the performance of both algorithms using the optical telescope network are shown and analyzed.