Master's degree theses

This thesis topic is issued in collaboration with Thales Alenia Space Italia

Abstract: The design of satellite platforms involves integrating various subsystems like power, propulsion, thermal management, and communication. As satellite missions grow in complexity, there is an increasing need for optimization tools that can enhance subsystem performance while balancing cost, efficiency, and reliability. This thesis proposes a tool for optimizing satellite platform design by analyzing and integrating the latest advancements in subsystem technologies. The goal is to develop a tool architecture that assists engineers in making informed decisions based on mission requirements and subsystem interactions.

Research Objectives:

Review current satellite subsystems (e.g., power, propulsion, communication) and their technological advancements.

Analyze interactions and trade-offs between subsystems in platform design.

Survey existing design and optimization tools in satellite engineering.

Develop a series of possible architecture that optimizes subsystem choices based on mission needs.

Methodology:

Literature Review: Investigate the state-of-the-art in satellite subsystem technologies.

Subsystem Analysis: Study how different subsystems interact and affect overall performance.

Tool Survey: Examine current satellite design and optimization tools.

Tool Architecture: Propose possible architectures that integrates subsystem constraints and mission goals.

Expected Outcomes:

A clear understanding of the latest satellite subsystem technologies.

A design and justification file of proposed design architectures.

The goal of this research is to explore novel techniques and develop an advanced deep learning model capable of detecting different satellite classes in space imagery. This multiclass satellite detector should be able to handle various satellite types, including different sizes, shapes, and orbital characteristics.
This research will contribute to the development of a generic multiclass satellite detector, enabling more comprehensive monitoring and analysis of satellite activities in space. The outcome of this thesis will have practical applications in satellite tracking, space debris monitoring, and overall space situational awareness.    This topic assumes a basic knowledge of Python. Participants will gain insights into the image formation and digitization process, along with hands-on experience in developing their own Deep Learning models using PyTorch, tailored specifically for the challenges of space environments.                                 

Apply deep learning techniques, specifically semantic segmentation, to synthetic satellite images. Focusing on developing models that can accurately segment and classify specific features or anomalies in the images of satellites will support the development of future on-orbit servicing and space debris removal initiatives.                    Participants will gain insights into the image formation and digitization process, along with hands-on experience in developing their own Deep Learning models using PyTorch, tailored specifically for the challenges of space environments.                              

Develop a real-time object detection system using PyTorch and deep learning models for space missions. Focus on optimizing the inference speed while maintaining high accuracy. Explore techniques such as model quantization, pruning, and parallelization to achieve real-time performance on space exploration devices. For this work we can buy a hardware accelerator for the better inference speed.                                                      Participants will gain insights into the image formation and digitization process, along with hands-on experience in developing their own Deep Learning models using PyTorch, tailored specifically for the challenges of space environments.     

The growing population of satellites in low Earth orbit implies an increasing likelihood of collisions. Tracking satellites through ground-based optical sensors is one way to face this challenge. Super Wide Field Of View (SWFOV) cameras allow to capture a great portion of the sky and to observe several satellites in one shot. This is the concept of the new project ASTRA by the INAF-OAS observatory in Loiano (BO). The workflow of the internship and thesis is divided into 4 parts:
1. Analysis of the literature on Space Situational Awareness, image processing techniques and astrometry;
2. Develop a Python code to be embedded in a Raspberry Pi computer for detection of tracklets in the images from the ASTRA SWFOV cameras;
3. Develop a Matlab or Python code for astrometry of the detected tracklets;
4. Write a Matlab or Python code for orbit association, using the astrometric measurements for initial orbit determination.

Tutor: Stefano Palmiotto

In the early stage of the definition of a space mission, it is often desirable to have a fast preliminary estimation of the DV cost of a low-thrust transfer. When the transfer is realised through a long spiral trajectory, a quick estimation of the total DV and transfer time can avoid lengthy calculations. For this reason, a number of authors have proposed simple control laws for the variation of specific orbital elements and/or analytical equations for the estimation of the DV associated to a given transfer. The usual approach to develop such approximations is that of integrating GPE using as integrand a fast anomaly variable while assuming the slow orbital elements as constants.

A recent approach developed in our lab proposes a more accurate analytical framework for integrating the GPE formulated as a system of linear differential equations. The thesis work consists of investigating the use of such a framework to develop analytical formulas for the secular orbital element variations, to be then used for the estimation of the DV associated to a given transfer.

Assessing and mitigating the risk of collisions between spacecraft in orbit, especially in LEO, is of paramount importance to ensure the safety of space assets, especially in the current context of the continuously increasing number of resident space objects. The common practice to evaluate the collision risk is by calculating the so-called 2D probability of collisions index, based on analysis of the closest approach between the two spacecraft in the so-called encounter plane. Although a 2D geometric analysis is suitable in most situations, some conjunctions require a full 3D analysis to assess the collision risk properly.

The thesis activity involves reviewing existing methods for 3D collision risk assessment and developing a detailed numerical simulator for assessing their performance. Extensive simulations in several representative conjunction scenarios will be performed to evaluate the respective advantages and drawbacks of the different methods. Areas of improvement shall also be identified.

Very Low Earth Orbits (VLEOs) offer a promising opportunity to enhance the quality of spaceborne Earth Observation data, primarily due to the higher ground resolution enabled by their extremely low altitude. However, the harsh environment characteristic of these orbits poses significant challenges for the spacecraft’s Attitude Determination and Control System (ADCS), potentially hindering the stabilization of the line-of-sight and degrading the effective image resolution.

In this context, the proposed internship and thesis work consist of two main parts:

1. The development of a MATLAB/Simulink-based simulator to model the spacecraft dynamics in VLEO, including the main disturbances affecting the satellite.
2. The preliminary design of a payload Line-of-Sight (LoS) stabilization system using miniaturized actuators.

After defining a reference orbit scenario, a numerical model of the LoS stabilization system will be implemented and tested within the developed simulator to assess its effectiveness and performance.

By addressing these challenges, this project aims to contribute to the advancement of attitude control strategies tailored for VLEO spacecraft.

One of the activities of the Microsatellite and Space Microsystem laboratory is the study and test of ADCS law on a attitude simulator testebd. The attitude simulator testbed is equipped with different subsystem for simulating the space environment (air bearing table, Helmholtz Cage, Sun simulator). Different mockups were used in the past for testing ADCS laws but until now no mockup included three ortogonal reaction wheels as well as a sun sensor. The task of the student should be the development of a ADCS 1U mockup equipped with reaction wheels and magnetorquers and a set of sensors.  The mockup should be equipped with a power source (batteries) and a communication module. The reaction wheels and magnetorquers should be sized in order to counteract the disturbance torques of the facility. The mockup should be sized following the CubeSat Design Specifications for a 1U CubeSat. The Mockup should be equipped with sensors for attitude determination (e.g. magnetometer, sun sensor, gyroscope, accelerometer and/or others) and actuators for attitude control (at least reaction wheels and magnetorquers). The thesis work should include a literature review of the existing solutions and trade off studies of alternative solutions.