Session
Technical Session VI: Enabling New Technologies and Methods I
Abstract
Low cost, fast access and multi-functional small satellites are being increasingly used to provide and exchange information for a wide variety of professions. They are particularly useful, for example, as a resource in very remote areas where they can provide useful information such as to rescue teams for changing conditions in a disaster zone and monitoring the sea state to warn approaching shipping. Unlike terrestrial communication systems, the receiver/transmitter in these di_erent application areas needs to be powered on and contact to specialised satellites to exchange data at speci_c time rather than consuming valuable power at all the time. This, therefore, requires accurate knowledge of when these satellites will pass over the horizon of the given location over a timescale of months in some cases. On the other hand, long term orbit estimation with high accuracy is also a key part for mission analysis and Earth observation operation planning. The same algorithm is also needed onboard satellites for autonomous on-board data management. The principal di_culty of predicting satellite passes over such long timescales is to take account of the e_ects of atmospheric drag. In this paper, we present a fast algorithm for the prediction of passes of a LEO satellite over any given location which provides high accuracy over a long period. The method exploits sophisticated analytic models of the orbit and provides direct computation of rise-set times and nadir tracking without the need of orbit propagation for hill climbing. This provides for a very small fast algorithm so more suitable for low-end computers and hand-held sets. Since the atmospheric drag is the key factor that a_ects the accuracy for long-term estimation for satellite in LEO, this model not only includes secular perturbation and periodic perturbations, on the other hand a drag model based on the well acknowledged NASA atmosphere statistics is incorporated. Di_erent from those in other orbit prediction methods, for example, the most widely used SGP4, the drag model here has a variable parameter which is subject to modify as time being on according to periodical atmosphere properties changing. Simulation result shows it can provide quite accurate estimation for long look-ahead period.
A Fast Prediction Algorithm of Satellite Passes
Low cost, fast access and multi-functional small satellites are being increasingly used to provide and exchange information for a wide variety of professions. They are particularly useful, for example, as a resource in very remote areas where they can provide useful information such as to rescue teams for changing conditions in a disaster zone and monitoring the sea state to warn approaching shipping. Unlike terrestrial communication systems, the receiver/transmitter in these di_erent application areas needs to be powered on and contact to specialised satellites to exchange data at speci_c time rather than consuming valuable power at all the time. This, therefore, requires accurate knowledge of when these satellites will pass over the horizon of the given location over a timescale of months in some cases. On the other hand, long term orbit estimation with high accuracy is also a key part for mission analysis and Earth observation operation planning. The same algorithm is also needed onboard satellites for autonomous on-board data management. The principal di_culty of predicting satellite passes over such long timescales is to take account of the e_ects of atmospheric drag. In this paper, we present a fast algorithm for the prediction of passes of a LEO satellite over any given location which provides high accuracy over a long period. The method exploits sophisticated analytic models of the orbit and provides direct computation of rise-set times and nadir tracking without the need of orbit propagation for hill climbing. This provides for a very small fast algorithm so more suitable for low-end computers and hand-held sets. Since the atmospheric drag is the key factor that a_ects the accuracy for long-term estimation for satellite in LEO, this model not only includes secular perturbation and periodic perturbations, on the other hand a drag model based on the well acknowledged NASA atmosphere statistics is incorporated. Di_erent from those in other orbit prediction methods, for example, the most widely used SGP4, the drag model here has a variable parameter which is subject to modify as time being on according to periodical atmosphere properties changing. Simulation result shows it can provide quite accurate estimation for long look-ahead period.
