A Far Range Image Processing Method for Autonomous Tracking of an Uncooperative Target
This paper proposes an image processing method for far range rendezvous. A target spacecraft at distances up to $30$~km is tracked from camera images. The method is based on linking bright, connected sets of pixels over sequences of images. Stars and the target spacecraft are identified by using a star catalog and performing motion segmentation. The algorithm is illustrated in detail and 
%the visibility of stars and of the target are analyzed which is a prerequisite to perform far range image processing. Further, 
results of a flight experiment called ARGON are presented. The experiment took place in the extended phase of the PRISMA mission. The image processing method was employed and a successful rendezvous from $30$~km to $3$~km was accomplished using optical navigation.
Advanced LIDAR-based techniques for autonomous navigation of spaceborne and airborne platforms
The main goal of this PhD thesis is the development and performance assessment of innovative techniques for the autonomous navigation of aerospace platforms by exploiting data acquired by electro-optical sensors. Specifically, the attention is focused on active LIDAR systems since they globally provide a higher degree of autonomy with respect to passive sensors. Two different areas of research are addressed, namely the autonomous relative navigation of multi-satellite systems and the autonomous navigation of Unmanned Aerial Vehicles. The global aim is to provide solutions able to improve estimation accuracy, computational load, and overall robustness and reliability with respect to the techniques available in the literature.
In the space field, missions like on-orbit servicing and active debris removal require a chaser satellite to perform autonomous orbital maneuvers in close-proximity of an uncooperative space target. In this context, a complete pose determination architecture is here proposed, which relies exclusively on three-dimensional measurements (point clouds) provided by a LIDAR system as well as on the knowledge of the target geometry. Customized solutions are envisaged at each step of the pose determination process (acquisition, tracking, refinement) to ensure adequate accuracy level while simultaneously limiting the computational load with respect to other approaches available in the literature. Specific strategies are also foreseen to ensure process robustness by autonomously detecting algorithms' failures. Performance analysis is realized by means of a simulation environment which is conceived to realistically reproduce LIDAR operation, target geometry, and multi-satellite relative dynamics in close-proximity. An innovative method to design trajectories for target monitoring, which are reliable for on-orbit servicing and active debris removal applications since they satisfy both safety and observation requirements, is also presented.
On the other hand, the problem of localization and mapping of Unmanned Aerial Vehicles is also tackled since it is of utmost importance to provide autonomous safe navigation capabilities in mission scenarios which foresee flights in complex environments, such as GPS denied or challenging. Specifically, original solutions are proposed for the localization and mapping steps based on the integration of LIDAR and inertial data. Also in this case, particular attention is focused on computational load and robustness issues. Algorithms' performance is evaluated through off-line simulations carried out on the basis of experimental data gathered by means of a purposely conceived setup within an indoor test scenario.
The microscale obstacle-resolving meteorological model MITRAS v2.0: model theory
Abstract. This paper describes the developing theory and underlying
processes of the microscale obstacle-resolving model MITRAS version 2. MITRAS
calculates wind, temperature, humidity, and precipitation fields, as well as
transport within the obstacle layer using Reynolds averaging. It explicitly
resolves obstacles, including buildings and overhanging obstacles, to
consider their aerodynamic and thermodynamic effects. Buildings are
represented by impermeable grid cells at the building positions so that the
wind speed vanishes in these grid cells. Wall functions are used to calculate
appropriate turbulent fluxes. Most exchange processes at the obstacle
surfaces are considered in MITRAS, including turbulent and radiative
processes, in order to obtain an accurate surface temperature. MITRAS is also
able to simulate the effect of wind turbines. They are parameterized using
the actuator-disk concept to account for the reduction in wind speed. The
turbulence generation in the wake of a wind turbine is parameterized by
adding an additional part to the turbulence mechanical production term in the
turbulent kinetic energy equation. Effects of trees are considered
explicitly, including the wind speed reduction, turbulence production, and
dissipation due to drag forces from plant foliage elements, as well as the
radiation absorption and shading. The paper provides not only
documentation of the model dynamics and numerical framework but also a solid
foundation for future microscale model extensions.
