Relating the dynamical frame and the ephemerides to the ICRF
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The recently successful flybys of Comet 103P/Hartley 2 by the Deep Impact spacecraft and Comet 9P/Tempel 1 by the Stardust spacecraft each presented different challenges from the perspective of comet ephemeris prediction and spacecraft targeting. Hartley 2 is a small, highly active comet, with nongravitational accelerations that proved very difficult to model, requiring some amount of "cometchasing" by the spacecraft navigators. In contrast, Tempel 1 is a far larger and less active comet. It showed very stable ephemeris behavior and at flyby was within 1-sigma of predictions issued more than a year prior to encounter. This happenstance was fortuitous because the Stardust spacecraft had very little fuel margin available for comet ephemeris errors.
The current statistical performances are shown for the TOPEX/POSEIDON operational orbit ephemeris (OOE) and ephemeris load for the onboard computer. Consequences of this performance for the extended mission are also discussed.
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An algorithm is proposed for generating sequences of Chebyshev series which are the best approximations of an astronomical ephemeris in the sense of Chebyshev over large intervals of time. The criterion for a polynomial approximation of a function to be the best polynomial approximation of the function is that the error function present certain rippling characteristics as described by Remez (1957). General features of the program in PL/1 are described.
The decrease in the semimajor axis of Lageos is considerably larger than expected. Gravitational effects, reference system effects, solar radiation pressure, Earth albedo pressure, neutral atmospheric drag, the Poynting Robertson Effect, and electrodynamic effects were used in explaining the observations. Quick look data provided are used to determine the Earth's polar motion and length of day. This process is routine, and provides these geophysical data every five days.
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The required knowledge of the Global Positioning System (GPS) satellite position accuracy can vary depending on a particular application. Application to relative positioning of receiver locations on the ground to infer Earth's tectonic plate motion requires the most accurate knowledge of the GPS satellite orbits. Research directed towards improving and evaluating the accuracy of GPS satellite orbits was conducted at the University of Texas Center for Space Research (CSR). Understanding and modeling the forces acting on the satellites was a major focus of the research. Other aspects of orbit determination, such as the reference frame, time system, measurement modeling, and parameterization, were also investigated. Gravitational forces were modeled by truncated versions of extant gravity fields such as, Goddard Earth Model (GEM-L2), GEM-T1, TEG-2, and third body perturbations due to the Sun and Moon. Nongravitational forces considered were the solar radiation pressure, and perturbations due to thermal venting and thermal imbalance. At the GPS satellite orbit accuracy level required for crustal dynamic applications, models for the nongravitational perturbation play a critical role, since the gravitational forces are well understood and are modeled adequately for GPS satellite orbits.
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Methods and apparatus for autonomous in-receiver prediction of orbit and clock states of Global Navigation Satellite Systems (GNSS) are described. Only the GNSS broadcast message is used, without need for periodic externally-communicated information. Earth orientation information is extracted from the GNSS broadcast ephemeris. With the accurate estimation of the Earth orientation parameters it is possible to propagate the best-fit GNSS orbits forward in time in an inertial reference frame. Using the estimated Earth orientation parameters, the predicted orbits are then transformed into Earth-Centered-Earth-Fixed (ECEF) coordinates to be used to assist the GNSS receiver in the acquisition of the signals. GNSS satellite clock states are also extracted from the broadcast ephemeris and a parameterized model of clock behavior is fit to that data. The estimated modeled clocks are then propagated forward in time to enable, together with the predicted orbits, quicker GNSS signal acquisition.