The Spacecraft Orbital Characterization Kit and its Applications to the CYGNSS Mission.

Abstract

The rapid increase of the number of objects in orbit around the Earth poses a serious threat to operational spacecraft and astronauts. In order to effectively avoid collisions, mission operators need to assess the risk of collision between the satellite and any other object whose orbit is likely to approach its trajectory. Several algorithms predict the probability of collision but have limitations that impair the accuracy of the prediction. An important limitation is that uncertainties in the atmospheric density are usually not taken into account in the propagation of the covariance matrix from current epoch to closest approach time. The atmosphere between 100 km and 700 km is strongly driven by solar and magnetospheric activity. Therefore, uncertainties in the drivers directly relate to uncertainties in the neutral density, hence in the drag acceleration. This results in important considerations for the prediction of Low Earth Orbits, especially for the determination of the probability of collision. This study shows how uncertainties in the atmospheric density can cause significant differences in the probability of collision and presents an algorithm that takes these uncertainties into account to more accurately assess the risk of collision. As an example, the effects of a geomagnetic storm on the probability of collision are illustrated. Plain Language Summary Spacecraft collision avoidance is particularly challenging at low altitudes (below 700 km). One of the main reasons is that, at these altitudes, satellite trajectories are strongly perturbed by atmospheric drag, a force particularly hard to model. The sources of errors mostly come from the complex coupling between the Sun and the Earth's environment. This system drives the density of the Earth's atmosphere on which the atmospheric drag directly depends. In other words, uncertainties in the atmospheric density result in large uncertainties in the satellite trajectories. The probability of collision, which is computed from the prediction of the satellite trajectories, thus cannot be predicted perfectly accurately. However, mission operators decide whether or not a collision avoidance maneuver has to be carried out based on the value of the probability of collision. Therefore, it is essential to characterize the level of uncertainty associated with the prediction of the probability of collision. The research presented here offers an approach to determine the uncertainty on the prediction of the probability of collision as a result of uncertainties in the atmospheric density. The ultimate goal is to assist mission operators in making the correct decision with regard to potential collision avoidance maneuvers.

Effects of Uncertainties in the Atmospheric Density on the Probability of Collision Between Space Objects

“…Developed in C, SpOCK supports parallelism and is therefore well suited for ensemble, Monte Carlo, or satellite constellation analysis. The algorithm and its different functionalities are presented in Bussy‐Virat et al ().…”

“…In all three cases, the time of close approach is 2 days after the epoch. The dynamic model used a spherical Earth with no perturbations, since the goal of this study was to validate the algorithm for computation of the probability of collision, while SpOCK's propagator and its dynamic model were validated in a previous study (Bussy-Virat et al, 2018). Figure 5 shows the cumulative distribution functions (CDFs) computed by SpOCK for Cases 1 (top), 6 (center), and 7 (bottom).…”

A Real-Time On-Orbit Signal Tracking Algorithm for GNSS Surface Observations

“…This has the significant advantage of reducing the specular point calculation duty cycle which can result in significant on-board savings of available computational resources. Additionally, there are publically available orbit propagation tools which can be integrated into a forward orbit prediction module, including the SGP4 [33] or SPoCK [34] orbit propagation tool sets.…”

A Simple Method for Correcting Empirical Model Densities During Geomagnetic Storms Using Satellite Orbit Data

“…This fails to capture how the changing composition of the thermosphere with altitude affects the gas‐surface interactions on the spacecraft faces, leading to a variable drag coefficient. The Spacecraft Orbital Characterization Kit (SpOCK), an orbital propagator developed at the University of Michigan, does not rely on B ∗ when using TLEs to perform orbit prediction (Bussy‐Virat, Getchius, & Ridley, 2018). Instead, it allows the user to describe the spacecraft geometry with a CAD file or describe the orientation and area of all the spacecraft faces manually.…”