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Advances in the Study of Intelligent Aerospace

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: 15 May 2025 | Viewed by 1652

Special Issue Editors


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Guest Editor
School of Aerospace Engineering, Tsinghua University, Beijing 100084, China
Interests: distributed space systems; system engineering of space scientific satellite; aerospace intelligence
Special Issues, Collections and Topics in MDPI journals
Mechanical and Manufacturing Engineering, University of New South Wales, Sydney 2052, Australia
Interests: space positioning; navigation & timing (PNT); space situational awareness (SSA)
Special Issues, Collections and Topics in MDPI journals
Department of Physics & Astronomy, University of Central Arkansas, Conway, AR 72035, USA
Interests: deep reinforcement learning; convolutional neural networks; variational autoencoders
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing is undergoing a profound transformation, extending beyond traditional methodologies to incorporate a diverse array of modern approaches. The integration of artificial intelligence (AI) into space remote sensing technology has been a pivotal driver for this transformation, heralding a new era of innovation and efficiency. Since the late 1970s, AI has been deployed to automate routine tasks and enhance navigational systems. In contemporary applications, AI confers numerous benefits, including heightened efficiency, enhanced safety, and the capacity to process vast datasets expeditiously. Within the realm of remote sensing and space operation, AI is revolutionizing applications such as predictive maintenance, autonomous navigation, and satellite data analysis, thereby significantly enhancing capabilities in satellite observation, space perception, and spacecraft control. These advancements not only augment the precision and effectiveness of remote sensing but also possess the potential to assist or even supplant humans in executing high-level mission operations autonomously. In the future, AI technologies in space remote sensing are anticipated to assume an increasingly critical role in the exploration, utilization, and development of space resources.

The primary aim of this Special Issue is to consolidate recent advancements and foster interdisciplinary collaboration among researchers and practitioners in the field. It aligns with the journal’s scope by promoting high-impact research that pushes the boundaries of aerospace science and technology. The Special Issue seeks to illuminate the transformative potential of artificial intelligence and its applications in aerospace, thereby contributing to the journal’s mission of disseminating pioneering research and practical insights.

By bringing together diverse perspectives and expertise, this Special Issue aims to provide a comprehensive overview of the current state of intelligent aerospace research and chart a course for future innovations. Submissions are encouraged to explore a variety of themes, including but not limited to, the following topics:

  • Application of machine vision in aerospace, especially in remote sensing.
  • Intelligent control of spacecraft and space robots.
  • Intelligent optimization methods for spacecraft trajectory.
  • Space object detection from optical images.
  • Machine learning for satellite behavior characterization.
  • Intelligent perception and control in planetary exploration.

Prof. Dr. Zhaokui Wang
Dr. Yang Yang
Dr. Lin Zhang
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

  • intelligent perception
  • space missions
  • remote sensing
  • intelligent control
  • machine learning
  • space object detection
  • planetary exploration
  • optical images

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Published Papers (1 paper)

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Research

21 pages, 6869 KiB  
Article
Research on Design and Staged Deployment of LEO Navigation Constellation for MEO Navigation Satellite Failure
by Wen Xue, Min Hu, Yongjing Ruan, Xun Wang and Moyao Yu
Remote Sens. 2024, 16(19), 3667; https://doi.org/10.3390/rs16193667 - 1 Oct 2024
Viewed by 971
Abstract
Low Earth orbit (LEO) satellites have unique advantages in navigation because of their high signal intensity and rapid geometric changes in a short period. In order to solve the problem of constellation performance degradation after a potential failure pertaining one or more medium [...] Read more.
Low Earth orbit (LEO) satellites have unique advantages in navigation because of their high signal intensity and rapid geometric changes in a short period. In order to solve the problem of constellation performance degradation after a potential failure pertaining one or more medium Earth orbit (MEO) navigation satellites, this paper designs the LEO navigation constellation and considers the task requirements of different stages of constellation deployment. Firstly, the LEO navigation constellation is designed by a non-dominated sorting genetic algorithm II (NSGA-II). The average position dilution of precision (PDOP) is 1.676, which is an improvement compared to the average PDOP offered by the four traditional GNSS. Secondly, the staged deployment of constellation takes into account the degradation of constellation performance caused by the failure of MEO navigation satellites, and the Monte Carlo method is used to analyze the case of three simultaneous satellite failures. The results show that a single satellite failure within each orbital plane and adjacent satellites with close phase separation has a great impact on the performance of the MEO navigation constellation. On this basis, a staged deployment strategy was adopted in order to balance cost, risk, and performance. The three phases deploy 66, 156, and 288 satellites, respectively; as a make-up constellation under contingencies, a navigation enhancement constellation, and an independent navigation constellation, the deployment of the staged sub-constellations meets the mission requirements. The constellation design and staged deployment method proposed in this paper can provide reference for the future study of LEO navigation constellations. Full article
(This article belongs to the Special Issue Advances in the Study of Intelligent Aerospace)
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