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Aerospace
Special Issues
Space Object Tracking
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Space Object Tracking

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

Deadline for manuscript submissions: 28 February 2027 | Viewed by 407

Special Issue Editors

Prof. Dr. Leping Yang

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Guest Editor
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
Interests: space security; orbital game and electromagnetic manipulation
Dr. Tao Lei

E-Mail Website
Guest Editor
Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 650500, China
Interests: photoelectric imaging and target recognition; intelligent image processing
Dr. Xi Long

E-Mail
Guest Editor
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
Interests: resource scheduling; maneuver detection; and fault diagnosis for space system

Special Issue Information

Dear Colleagues,

With the rapid development of the global aerospace industry and the growing number of on-orbit space objects, orbital congestion, collision risks and space debris hazards have become critical challenges restricting the sustainable development of space activities. As the core technical pillar of space situational awareness, space object tracking becomes increasingly important, as it covers the whole chain of orbital dynamics analysis, multi-source sensor information processing, space surveillance network operation and intelligent decision-making.

We invite you to submit high-quality original research articles and review manuscripts to the Special Issue of Aerospace entitled Space Object Tracking and share your latest theoretical research, technological breakthroughs and engineering applications in this field.

This Special Issue will gather global cutting-edge research results and practical experience in the field of space object tracking; build a high-level academic exchange platform for researchers and engineers engaged in aerospace engineering, orbital dynamics, space surveillance and related disciplines; and promote the cross-integration and innovative development of key technologies in this domain.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • Orbital dynamics modeling and high-precision orbit determination;
  • Space surveillance network resource scheduling and optimization;
  • Space object maneuver detection and trajectory analysis;
  • Multi-source sensor information fusion and processing;
  • Image processing for space target sensing and recognition;
  • Space object intent recognition and risk assessment;
  • Tracking data error analysis and correction;
  • Space debris tracking and early warning technology.

We look forward to receiving your contributions.

Prof. Leping Yang
Dr. Tao Lei
Dr. Xi Long
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 object tracking
  • orbital dynamics space
  • surveillance network
  • orbit determination maneuver
  • detection sensor information
  • processing space safety

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Published Papers (1 paper)

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Review

32 pages, 1018 KB  
Review
Photometric Characterization of Space Objects: From Classical BRDF Models to Data-Driven Prediction
by Liu Yang, Can Xu and Yasheng Zhang
Aerospace 2026, 13(5), 418; https://doi.org/10.3390/aerospace13050418 - 29 Apr 2026
Viewed by 150
Abstract
The rapid proliferation of resident space objects has made space situational awareness critically dependent on accurate characterization of non-cooperative targets using photometric light curves. This review provides a comprehensive examination of data-driven approaches for space object photometric prediction, synthesizing research across optical scattering [...] Read more.
The rapid proliferation of resident space objects has made space situational awareness critically dependent on accurate characterization of non-cooperative targets using photometric light curves. This review provides a comprehensive examination of data-driven approaches for space object photometric prediction, synthesizing research across optical scattering characterization, shape and attitude inversion methodologies, and intelligent analysis techniques based on machine learning and deep learning. The evolution from traditional physics-based models to contemporary data-driven paradigms is systematically analyzed, revealing fundamental trade-offs between physical interpretability, computational efficiency, and predictive accuracy. Key findings indicate that while physical bidirectional reflectance distribution function (BRDF) models provide rigorous foundations, their computational demands and prior knowledge requirements limit operational applicability; conversely, deep learning has demonstrated superior predictive accuracy in existing comparative studies, although this conclusion is qualified by the absence of standardized public benchmarks, and it also suffers from interpretability deficits and simulation-to-reality generalization gaps. Critical research gaps are identified, including the absence of public benchmark datasets, inadequate handling of temporal multi-scale phenomena, and the persistent challenge of bridging simulated and real-world observations. Future directions should pursue physics-guided machine learning frameworks that integrate domain knowledge with data-driven capabilities, develop explainable artificial intelligence techniques tailored for photometric analysis, and establish standardized evaluation protocols to advance next-generation space object characterization essential for collision avoidance and space traffic management. Full article
(This article belongs to the Special Issue Space Object Tracking)
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