Space weather is the main source of uncertainty in the position of all objects in low Earth orbit (LEO) below about 1,000 km. The main impact is strong variation in the neutral density of the thermosphere as it responds to radiative inputs from the Sun in the extreme ultraviolet wavelength range, energetic particle precipitation in the high-latitude auroral zones, and global-scale electrical currents generated during geomagnetic storms. Waves and instabilities from the lower atmosphere can also influence thermospheric density in complex ways. The variation in neutral density leads to variable drag forces on satellites flying through the thermosphere, which in turn causes orbital track changes. We currently lack the ability to accurately model and predict the neutral density changes in the thermosphere in response to space weather inputs. Operational empirical models of thermospheric density are inaccurate during space weather events, and mandate that LEO orbital tracks carry large "error ellipsoids" around all objects to account for positional uncertainty. This leads to many more "conjunction" warnings than necessary as large error ellipsoids are frequently calculated to intersect in orbit. As the LEO domain becomes more crowded with the advent of commercial "megaconstellations" we face a growing challenge to reduce orbital uncertainties by developing whole atmosphere models to enable timely and accurate forecasts of thermospheric conditions. We recommend that researchers, forecasters, and policy makers coordinate to ensure that space weather research and forecasting is tightly integrated into upcoming changes to the operational Space Traffic Management system.
Recent advances in robotics and computer vision have enabled the implementation of sophisticated vision-based relative navigation algorithms for robotic spacecraft using a single calibrated monocular camera. These techniques, initially developed for ground robots, show great promise for robotic spacecraft applications. However, several challenges still exist, which hinder the direct use of these approaches in the space environment without further modifications. For example, the use of a monocular camera for robotic spacecraft operations with respect to a known target configuration may be limited owing to the abrupt illumination changes in a low-Earth orbit, long duration target tracking requirements during large target image change in scale, background outliers, and the necessity to perform (semi)autonomous relative navigation in the presence of limited resources (fuel, onboard computer hardware, etc). This paper proposes a relative navigation scheme in space that makes use of three different ingredients. First, two different feature detectors are used to ensure reliable feature detection over diverse distances, and subsequently fast feature selection/filtering is applied to detect the visual features of the fiducial marker. Next, a feature-pattern matching algorithm via robust affine registration is used for relative navigation to achieve robust automated re-acquisition in case of a lost target. Finally, a probabilistic graphical model-based fixed-lag smoothing based on factor graphs is used to accurately propagate relative translation and orientation 6-DOF state estimates and their velocities. The proposed approach is validated on hardware-in-the-loop 5-DOF spacecraft simulation facility at Georgia Tech.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.