arXiv subjects

Andrea De Vittori

Publications and source records attributed to Andrea De Vittori.

3 recordsLinked to original sources

arXiv2026

Two-Filter Adaptive Gaussian Mixture Smoothing for Nonlinear Systems

Space object tracking poses challenging estimation problems due to the significantly non-Gaussian distributions that can arise, particularly under highly nonlinear dynamics or during periods of measurement unavailability. Adaptive Gaussian mixture filters can dynamically adjust their mixture resolution to systematically approximate these non-Gaussian distributions, but challenging estimation problems can still produce highly uncertain or inaccurate estimates, especially during prolonged observation gaps. Smoothing algorithms can significantly improve filtered estimates by incorporating future measurement information for applications where immediacy is not required, but smoothing in nonlinear, non-Gaussian settings poses additional theoretical and computational challenges. This work develops a new recursive Bayesian smoothing algorithm for nonlinear systems that refines Gaussian mixture posteriors produced by a forward adaptive Gaussian mixture filter. A two-filter smoothing approach approximates the future measurement information by an information-form Gaussian mixture in the state variable. Techniques from nonlinear Gaussian mixture filtering including splitting, merging, and recursive measurement updating are also incorporated to improve the accuracy and computational efficiency of this approximation. The proposed smoother's estimation capabilities are demonstrated on space object tracking problems for Molniya and Earth-Moon halo orbits and shown to significantly reduce estimation error and uncertainty compared to the forward filter.

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arXiv2025

CAMmary: A Review of Spacecraft Collision Avoidance Manoeuvre Design Methods

Ensuring safety for spacecraft operations has become a paramount concern due to the proliferation of space debris and the saturation of valuable orbital regimes. In this regard, the Collision Avoidance Manoeuvre (CAM) has emerged as a critical requirement for spacecraft operators, aiming to efficiently navigate through potentially hazardous encounters. Currently, when a conjunction is predicted, operators dedicate a considerable amount of time and resources to designing a CAM. Given the increased frequency of conjunctions, autonomous computation of fuel-efficient CAMs is crucial to reduce costs and improve the performance of future operations. To facilitate the transition to an autonomous CAM design, it is useful to provide an overview of its state-of-the-art. In this survey article, a collection of the most relevant research contributions in the field is presented. We review and categorize existing CAM techniques based on their underlying principles, such as (i) analytic, semi-analytic, or numerical solutions; (ii) impulsive or continuous thrust; (iii) deterministic or stochastic approaches, (iv) free or fixed manoeuvring time; (v) free or fixed thrust direction. Finally, to determine the validity of the algorithms potentially implementable for autonomous use, we perform a numerical comparison on a large set of conjunctions. With this analysis, the algorithms are evaluated in terms of computational efficiency, accuracy, and optimality of the computed policy. Through this comprehensive survey, we aim to provide insights into the state-of-the-art CAM methodologies, identify gaps in current research, and outline potential directions for future developments in ensuring the safety and sustainability of spacecraft operations in increasingly congested orbital environments.

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arXiv2024

Collision Avoidance Maneuvers Optimization in the Presence of Multiple Encounters

The optimization of fuel-optimal low-thrust collision avoidance maneuvers (CAMs) in scenarios involving multiple encounters between spacecraft is addressed. The optimization's objective is the minimization of the total fuel consumption while respecting constraints on the total probability of collision. The solution methodology combines sequential convex programming, second-order cone programming, and differential algebra to approximate the non-convex optimal control problem progressively. A Gaussian mixture model method is used to propagate the initial covariance matrix of the secondary spacecraft, allowing us to split it into multiple mixands that can be treated as different objects. This leads to an accurate propagation of the uncertainties. No theoretical guarantee is given for the convergence of the method to the global optimum of the original optimal control problem. Nonetheless, good performance is demonstrated through case studies involving multiple short- and long-term encounters, showcasing the generation of fuel-efficient CAMs while respecting operational constraints.

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