Perugino, Lorenzo (2025) Maneuver Detection And Pattern Of Life Estimation For Enhanced Space Situational Awareness. [Tesi di dottorato]
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| Tipologia del documento: | Tesi di dottorato |
|---|---|
| Lingua: | English |
| Titolo: | Maneuver Detection And Pattern Of Life Estimation For Enhanced Space Situational Awareness |
| Autori: | Autore Email Perugino, Lorenzo lorenzo.perugino@unina.it |
| Data: | 10 Dicembre 2025 |
| Numero di pagine: | 133 |
| Istituzione: | Università degli Studi di Napoli Federico II |
| Dipartimento: | Ingegneria Industriale |
| Dottorato: | Ingegneria aerospaziale, navale e della qualità |
| Ciclo di dottorato: | 38 |
| Coordinatore del Corso di dottorato: | nome email Grassi, Michele michele.grassi@unina.it |
| Tutor: | nome email Giancarmine, Fasano [non definito] Giorgio, Isoletta [non definito] Antonio, Romano [non definito] |
| Data: | 10 Dicembre 2025 |
| Numero di pagine: | 133 |
| Parole chiave: | Astrodynamics, Space Situational Awareness, Space Surveillance and Tracking, Maneuver Detection, Maneuver Classification, Pattern of Life estimation, Anomaly detection. |
| Settori scientifico-disciplinari del MIUR: | Area 09 - Ingegneria industriale e dell'informazione > ING-IND/05 - Impianti e sistemi aerospaziali |
| Informazioni aggiuntive: | Il numero di pagine non tiene conto del frontespizio ed indice. Contando anche quelli le pagine salgono a 137. Inoltre il caso test GEO del capitolo di Pattern of Life generation (precisamente sezione 5.2.1.2) è stato rimosso per dubbi sulla correttezza dei dati. |
| Depositato il: | 19 Dic 2025 13:33 |
| Ultima modifica: | 12 Ago 2026 05:37 |
| URI: | https://www.fedoa.unina.it/id/eprint/16008 |
Abstract
The main objective of this PhD activity is the development of innovative techniques and strategies to enable new approaches in the domain of Space Situational Awareness (SSA), with a particular focus on Space Surveillance and Tracking (SST). The increasing number of Resident Space Objects (RSOs) in Earth orbit makes the capability to maintain accurate knowledge of their positions and behaviors increasingly important. A significant portion of these objects has maneuvering capabilities, causing traditional orbital propagators to be insufficient for reliable tracking. This PhD thesis addresses this challenge through the development of methods and algorithms for maneuver detection and classification. These algorithms are designed to process heterogeneous orbital datasets and encompass three different maneuver detection methods to identify potential orbital maneuvers. The maneuvers are then classified according to their types, based on the affected orbital parameters, and a confidence score is assigned. In addition, a novel algorithm has been developed to extend this framework by characterizing the recurrent behavioral patterns of maneuvering satellites, referred to as Pattern of Life (PoL). By understanding the typical maneuver behavior of a satellite, it becomes possible to compare new detections with historical patterns and to distinguish between nominal and anomalous maneuvers. This approach provides a valuable contribution to the monitoring of satellite behavior, supporting the development of more advanced and reliable Space Surveillance and Tracking capabilities.
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