As satellite systems become more and more complex and interact with each other in space, close proximity operation becomes an important aspect of many satellite missions. Simultaneously, systems are becoming increasingly autonomous, for example in rendezvous and docking operation
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As satellite systems become more and more complex and interact with each other in space, close proximity operation becomes an important aspect of many satellite missions. Simultaneously, systems are becoming increasingly autonomous, for example in rendezvous and docking operations. This poses harsh requirements for the guidance, navigation and control systems on-board of satellites, and especially on satellite navigation filterswhich estimate the state of the satellite and of other systems and objects that it is interactingwith, often based on information input from visual sensors. The commonly used Extended Kalman Filter (EKF) performs well but is not necessarily ideally suited for these emerging challenges, which is why the viability of potential filter alternatives in close-proximity satellite operations was studied. The project was conducted in cooperation with DLR Oberpfaffenhofen in Germany, where currently an EKF is used in close-proximity satellite operations. This filter serves as a baseline for performance testing conducted throughout the project. Potential filter alternatives were identified based on an extensive background study and the mission needs for close-proximity satellite operation. Furthermore, the purpose of the project is to identify and document concrete mismatches between the different testing methods used to serve as a reference in future projects. From the background study two filters, the Extended Kalman Filter with intermediate smoothing step (EKFS) and the Unscented Kalman Filter (UKF) were selected based on a qualitative performance trade-off that focussed on the expected performance under the demands of closeproximity operation in space. The filters were judged based on the available documentation. For the selected filters, as well as for the EKF currently used by DLR, performance criteria were formulated, with a focus on the accuracy of satellite state parameter estimation and filter convergence speed. To collect test data, two approaches were taken: a newly developed simulation test assessing the theoretical performance of the filters in different test scenarios; and a hardware-in-the-loop test in the EPOS 2.0 facility in Oberpfaffenhofen where approaches using two physical satellite models can be performed. The latter test is used to identify problems in the filter performance that have not been found using the simulation test and to validate the filters for more representative real-world performance. An analysis of the test results from the simulation performance test have shown that the EKFS and the UKF can outperform the EKF in the convergence speed and the estimation of some, but not all satellite state parameters. However, it was also identified that the UKF using its current implementation struggles to assess the attitude of the satellite state accurately. Apart from the attitude estimation from the UKF the filters were considered verified and were implemented in the hardware test facility. The hardware test could not confirm the previously seen performance consistently and both filters showed state estimation divergence at closeproximity of the satellites. Thus, their performance could not be validated and they cannot yet under the current implementation be called viable filter alternatives to the EKF. This is due to the fact that sudden state estimation divergence is potentially catastrophic, especially at close distances of the satellites. In addition, differences in the quality of the measurements between the tests of the different filters highlighted potential problems in the comparability of test results. It was concluded that the UKF is the more promising alternative filter for the future since it showed better performance in the hardware test prior to divergence than the EKF and outperformed the EKFS in all hardware tests. In addition it converged the fastest of the three filters. Further studies need to be performed to correct implementation problems, however. Possible approaches for validating the suggested approaches are presented. Different test approaches should also be taken to make the hardware tests more representative and the simulation test should be updated to be more reflective of real world conditions. Several observations on the challenges in moving from simulation to hardware testingwere identified and are presented. Primarily, challenges were found to arise from the faulty selection of test cases for comparability, the neglect of certain inputs observed in hardware testing with previously unpredicted effects in the simulation test and the continuous change of inputs in the real world which were modelled constant in the simulation test.
