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Aerospace
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Advances in Space Surveillance and Tracking
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Advances in Space Surveillance and Tracking

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Astronautics & Space Science".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 6097

Special Issue Editors

Dr. Marco Felice Montaruli

E-Mail Website
Guest Editor
Department of Aerospace Science and Technology, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy
Interests: space situational awareness; space surveillance and tracking; orbit determination; radar array technologies for space surveillance; cislunar domain awareness
Dr. Mauro Massari

E-Mail Website
Guest Editor
Department of Aerospace Science and Technology, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy
Interests: space situational awareness; space surveillance and tracking; orbit determination; proximity operations; GNC; active debris removal; in-orbit servicing; space robotics

Special Issue Information

Dear Colleagues,

In recent decades, the proliferation of resident space objects has become a major concern for space agencies and institutions all around the world, due to the presence of both operational satellites, defunct satellites, rocket bodies, and debris. The latter can either be parts of satellites or fragments generated by in-orbit break-ups, caused by collisions between satellites or explosions. On one hand, this motivates the development of the know-how to perform autonomous in-orbit operations, such as active debris removal or in-orbit servicing, by pushing for proximity operation technologies. On the other hand, it is fundamental to build up and maintain resident space object catalogues, to monitor and manage the increased risk posed by the increased space traffic. Within this framework, first, there is a need for orbit determination techniques both to detect uncatalogued space objects and to monitor catalogued ones. This also includes techniques to correlate measurements with catalogued objects and to discover and characterise eventual unforeseen manoeuvres. A second task is related to operational actions, such as monitoring the re-entry of satellites on the ground, detecting and characterising satellite fragmentations, and identifying and mitigating collisions. In recent years, the cataloguing workflow through measurements acquired on board satellites has been investigated, opening up space-based applications of the aforementioned techniques. This also makes it possible to extend space surveillance applications to lunar and cislunar regions.

This Special Issue invites submissions on state-of-the-art techniques and technologies related to the above space surveillance applications. We encourage submissions that extend the current state of the art and explore innovative approaches and algorithms. In this context, we are particularly interested in (but not limited to) papers that propose and analyse methodologies using artificial intelligence-based techniques. With this Special Issue, we aim at stimulating novelty and paradigm shifts in the discipline of space surveillance and tracking.

Dr. Marco Felice Montaruli
Dr. Mauro Massari
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • space debris
  • space surveillance and tracking
  • space situational awareness
  • orbit determination
  • space objects cataloguing
  • artificial intelligence
  • in-orbit fragmentations
  • satellite conjunctions
  • collision avoidance
  • space traffic management
  • cislunar region

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Published Papers (7 papers)

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Research

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13 pages, 1393 KB  
Article
Distribution and Evolution of the Debris Cloud from the Fragmentation of Intelsat 33E
by Peng Shu, Meng Zhao, Yuyan Wu, Zhen Yang and Yuqiang Li
Aerospace 2026, 13(4), 303; https://doi.org/10.3390/aerospace13040303 - 25 Mar 2026
Viewed by 388
Abstract
The breakup of Intelsat 33E on 19 October 2024 posed a potential risk to satellites in the Geostationary Earth Orbit (GEO). This study analyzes the evolution and distribution of these fragments using a probabilistic approach. The initial distribution of the fragments, derived from [...] Read more.
The breakup of Intelsat 33E on 19 October 2024 posed a potential risk to satellites in the Geostationary Earth Orbit (GEO). This study analyzes the evolution and distribution of these fragments using a probabilistic approach. The initial distribution of the fragments, derived from the NASA Standard Breakup Model, indicates the generation of 4393 fragments larger than 1 cm. The spatial propagation of these fragments is modeled analytically in the Earth-Centered Earth-Fixed reference frame, showing the formation of high-density ring structures in the equatorial plane from 24 h to 28 days after the breakup. The orbits of 36 cataloged fragments are retrieved and compared with the probability density. Furthermore, Monte Carlo simulations validate the probabilistic model and highlight its efficiency in capturing low-probability events. Collision risks to other GEO satellites are assessed, showing that the top 10% of satellites encounter a collision probability of up to 10−8 after 28 days. Satellites near the equatorial plane are at higher risk, whereas those with higher inclinations are less affected. These findings underscore the need for enhanced monitoring and mitigation strategies for GEO breakup events, given the challenges in detecting small fragments. Full article
(This article belongs to the Special Issue Advances in Space Surveillance and Tracking)
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27 pages, 4763 KB  
Article
Orbit-Prior-Guided Target-Centered Stacking for Space Surveillance and Tracking Under Dynamic-Platform Optical Observations
by Lanze Qu, Junchi Liu, Hongwen Li, Zhiyong Wu, Jianli Wang and Kainan Yao
Aerospace 2026, 13(3), 279; https://doi.org/10.3390/aerospace13030279 - 17 Mar 2026
Viewed by 320
Abstract
In visible-light optical observations for Space Surveillance and Tracking (SST) from ground-based dynamic platforms, attitude disturbances and field-of-view discontinuities frequently undermine interframe geometric consistency, leading to energy diffusion and unstable gain in multi-frame stacking for faint space objects. We propose orbit-prior- guided target-centered [...] Read more.
In visible-light optical observations for Space Surveillance and Tracking (SST) from ground-based dynamic platforms, attitude disturbances and field-of-view discontinuities frequently undermine interframe geometric consistency, leading to energy diffusion and unstable gain in multi-frame stacking for faint space objects. We propose orbit-prior- guided target-centered stacking (OPG-TCS), a tracking-oriented post-processing method designed to stabilize target energy accumulation and improve enhancement reliability under dynamic observation conditions. OPG-TCS performs frame-wise astrometric calibration using star fields (WCS) and leverages projected orbit priors to predict target pixel locations, enabling local cropping and target-centered alignment/stacking without relying on full-frame geometric consistency. We evaluate OPG-TCS on multiple real-world dynamic-platform sequences and compare it with direct stacking and representative robust baselines. Signal-to-noise ratio (SNR) is used as the primary metric, while auxiliary indicators of peak prominence, energy concentration, and shape consistency are employed to assess robustness across varying stacking depths. The results show that OPG-TCS provides stable enhancement over different frame counts; in representative 50-frame fusions, its relative SNR surpasses direct stacking by 33.7–97.8%. These findings suggest that OPG-TCS offers a practical and robust enhancement strategy for SST-oriented observation of faint space objects, supporting more reliable detection and subsequent tracking analysis. Full article
(This article belongs to the Special Issue Advances in Space Surveillance and Tracking)
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25 pages, 5648 KB  
Article
Advanced Sensor Tasking Strategies for Space Object Cataloging
by Alessandro Mignocchi, Sebastian Samuele Rizzuto, Alessia De Riz and Marco Felice Montaruli
Aerospace 2026, 13(1), 81; https://doi.org/10.3390/aerospace13010081 - 12 Jan 2026
Viewed by 834
Abstract
Space Surveillance and Tracking (SST) plays a crucial role in ensuring space safety. To this end, accurate and numerous observational resources are needed to build and maintain a catalog of space objects. In particular, it is essential to develop optimal observation strategies to [...] Read more.
Space Surveillance and Tracking (SST) plays a crucial role in ensuring space safety. To this end, accurate and numerous observational resources are needed to build and maintain a catalog of space objects. In particular, it is essential to develop optimal observation strategies to maximize both the number and the quality of detections obtained from a sensor network. This represents a key step in the assessment of the network through simulations. This work presents the integrated development of sensor tasking strategies for optical systems and a track-to-track correlation pipeline within SΞNSIT, a software environment designed to simulate sensor network configurations and evaluate cataloging performance. For high-altitude low Earth orbit (HLEO) targets, which are fast-moving and widely distributed, tasking strategies emphasize systematic scans of the Earth’s shadow boundary to exploit favorable phase angles and improve observational accuracy, while medium- and geostationary-Earth orbits (MEO–GEO) rely on equatorial-plane scans. The correlation pipeline employs Two-Body Integrals, uncertainty propagation, and a χ2-test with the Squared Mahalanobis Distance to associate tracks and perform initial orbit determination of newly detected objects. Results indicate that the integrated approach significantly enhances detection coverage, leading to greater catalog build-up efficiency and improved SST performance. Consequently, it facilitates the cataloging of numerous uncataloged objects within a reduced timeframe. Full article
(This article belongs to the Special Issue Advances in Space Surveillance and Tracking)
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26 pages, 3882 KB  
Article
Trading Off Accuracy and Runtime in Orbit Propagation to Enhance Satellite Mission Operations
by Arianna Rigo, João Paulo Monteiro, Rodrigo Ventura and Paulo J. S. Gil
Aerospace 2026, 13(1), 8; https://doi.org/10.3390/aerospace13010008 - 23 Dec 2025
Viewed by 967
Abstract
In this work, we evaluate the impact of numerical integration methods and perturbation models on the computational speed and position accuracy of orbit propagation techniques. With increasing numbers of satellites in orbit, space traffic management may require near real-time satellite operations, for which [...] Read more.
In this work, we evaluate the impact of numerical integration methods and perturbation models on the computational speed and position accuracy of orbit propagation techniques. With increasing numbers of satellites in orbit, space traffic management may require near real-time satellite operations, for which computational speed may play a more important part in orbit propagation than positional accuracy. The aim of this work is to identify the most suitable propagation parameters for different mission scenarios and outline the perturbations to be considered based on the target orbit characteristics. We analyze the impact of the integrators’ tolerance on accuracy and runtime, as well as quantify the dominant perturbations for each orbit type. We use a Starlink satellite as a reference case, propagating it across multiple orbital regimes. The results are presented in the form of Pareto fronts trading off runtime and positional accuracy. These Pareto fronts outline some important results, for instance, how gravitational models beyond 32×32 yield no accuracy improvements while significantly increasing runtime. We also verify that drag is critical in VLEO, LEO, SSO, and HEO (Molniya), while third-body effects play a major role in HEO (Molniya and Tundra), GEO, and GSO, and solar radiation pressure becomes significant in HEO (Tundra), GEO, and GSO. These results can be incorporated into collision avoidance optimization strategies for real-time satellite operations, thereby contributing to more efficient space traffic management. Full article
(This article belongs to the Special Issue Advances in Space Surveillance and Tracking)
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32 pages, 12377 KB  
Article
Joint Estimation of Attitude and Optical Properties of Uncontrolled Space Objects from Light Curves Considering Atmospheric Effects
by Jorge Rubio, Adrián de Andrés, Carlos Paulete, Ángel Gallego and Diego Escobar
Aerospace 2025, 12(10), 942; https://doi.org/10.3390/aerospace12100942 - 19 Oct 2025
Viewed by 977
Abstract
The unprecedented increase in the number of objects orbiting the Earth necessitates a comprehensive characterisation of these objects to improve the effectiveness of Space Surveillance and Tracking (SST) operations. In particular, accurate knowledge of the attitude and physical properties of space objects has [...] Read more.
The unprecedented increase in the number of objects orbiting the Earth necessitates a comprehensive characterisation of these objects to improve the effectiveness of Space Surveillance and Tracking (SST) operations. In particular, accurate knowledge of the attitude and physical properties of space objects has become critical for space debris mitigation measures, since these parameters directly influence major perturbation forces like atmospheric drag and solar radiation pressure. Characterising a space object beyond its orbital position improves the accuracy of SST activities such as collision risk assessment, atmospheric re-entry prediction, and the design of Active Debris Removal (ADR) and In-Orbit Servicing (IOS) missions. This study presents a novel approach for the simultaneous estimation of the attitude and optical reflective properties of uncontrolled space objects with known shape using light curves. The proposed method also accounts for atmospheric effects, particularly the Aerosol Optical Depth (AOD), a highly variable parameter that is difficult to determine through on-site measurements. The methodology integrates different estimation, optimisation, and data analysis techniques to achieve an accurate, robust, and computationally efficient solution. The performance of the method is demonstrated through the analysis of a simulated scenario representative of realistic operational conditions. Full article
(This article belongs to the Special Issue Advances in Space Surveillance and Tracking)
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21 pages, 3803 KB  
Article
Optimization of a Walker Constellation Using an RBF Surrogate Model for Space Target Awareness
by You Fu, Zhaojing Xu, Youchen Fan, Liu Yi, Zhao Ma, Yuhai Li and Shengliang Fang
Aerospace 2025, 12(10), 933; https://doi.org/10.3390/aerospace12100933 - 16 Oct 2025
Cited by 2 | Viewed by 1201
Abstract
Designing Low Earth Orbit (LEO) constellations for the continuous, collaborative observation of space objects in MEO/GEO is a complex optimization task, frequently limited by prohibitive computational costs. This study introduces an efficient surrogate-based framework to overcome this challenge. Our approach integrates Optimized Latin [...] Read more.
Designing Low Earth Orbit (LEO) constellations for the continuous, collaborative observation of space objects in MEO/GEO is a complex optimization task, frequently limited by prohibitive computational costs. This study introduces an efficient surrogate-based framework to overcome this challenge. Our approach integrates Optimized Latin Hypercube Sampling (OLHS) with a Radial Basis Function (RBF) model to minimize the required number of satellites. In a comprehensive case study targeting 18 diverse space objects—including communication satellites in GEO (e.g., EUTELSAT, ANIK) and navigation satellites in MEO/IGSO from GPS, Galileo, and BeiDou constellations—the method proved highly effective and scalable. It successfully designed a 208-satellite Walker constellation that provides 100% continuous coverage over a 36-h period. Furthermore, the design ensures that each target is simultaneously observed by at least three satellites at all times. A key finding is the method’s remarkable efficiency and scalability: the optimal solution for this larger problem was found using only 46 high-fidelity function evaluations, maintaining a computational time that was 5–8 times faster than traditional global optimization algorithms. This research demonstrates that surrogate-assisted optimization can drastically lower the computational barrier in constellation design, offering a powerful tool for building cost-effective and robust Space Situational Awareness (SSA) systems. Full article
(This article belongs to the Special Issue Advances in Space Surveillance and Tracking)
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Review

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28 pages, 2467 KB  
Review
Light-Curve Classification of Resident Space Objects for Space Situational Awareness: A Scoping Review
by Minyoung Hwang, Vithurshan Suthakar, Randa Qashoa, Regina S. K. Lee and Gunho Sohn
Aerospace 2026, 13(3), 287; https://doi.org/10.3390/aerospace13030287 - 18 Mar 2026
Viewed by 490
Abstract
The proliferation of Resident Space Objects (RSOs), including satellites, rocket bodies, and debris, poses escalating challenges for Space Situational Awareness (SSA). Optical light curves capture temporal brightness variations influenced by factors such as attitude variation, viewing geometry, and surface properties. When appropriately processed [...] Read more.
The proliferation of Resident Space Objects (RSOs), including satellites, rocket bodies, and debris, poses escalating challenges for Space Situational Awareness (SSA). Optical light curves capture temporal brightness variations influenced by factors such as attitude variation, viewing geometry, and surface properties. When appropriately processed and analyzed, these data can support RSO characterization and classification. This paper presents a scoping review of machine learning (ML) and deep learning (DL) methods for RSO classification using light-curve data. From 297 peer-reviewed studies published between 2014 and 2025, a screened subset of 29 works is selected for detailed methodological comparison. We trace the methodological evolution from handcrafted feature engineering toward convolutional, recurrent, and self-supervised models that learn representations directly from photometric time series. An analysis of three publicly accessible databases, Mini Mega TORTORA, Space Debris Light-Curve Database, and Ukrainian Database, reveals pronounced class imbalance, with payloads comprising over 80% of observations. While models trained on simulated data routinely achieve 95 to 99% accuracy, performance on measured light curves degrades to 75 to 92%, exposing a persistent gap between simulation and observation. We further identify data scarcity, repeated observations of the same objects, and inconsistent evaluation protocols as key barriers to reproducible benchmarking. Future progress will require benchmark-ready, sensor-aware datasets spanning diverse orbital regimes and viewing geometries, alongside physics-informed and transfer-learning approaches that improve robustness across sensors and between synthetic and observational domains. Full article
(This article belongs to the Special Issue Advances in Space Surveillance and Tracking)
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