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CubeSats Applications and Technology
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CubeSats Applications and Technology

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

Deadline for manuscript submissions: closed (15 February 2023) | Viewed by 15209

Special Issue Editors

Prof. Dr. Zhaokui Wang

E-Mail Website
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
Prof. Dr. Xiaozhou Yu

E-Mail Website
Guest Editor
School of Aeronautics and Astronautics, Dalian University of Technology, Dalian 116024, China
Interests: space system engineering; CubeSat technology
Dr. Lin Zhang

E-Mail Website
Guest Editor
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
Dr. Farid Gamgami

E-Mail Website
Guest Editor
CSO, Berlin Space Technologies, Max-Planck-Straße 3, 12489 Berlin, Germany
Interests: space system engineering; space environment and technology

Special Issue Information

Dear Colleagues,

CubeSats are a class of nanosatellites built to standard dimensions. Since the concept was proposed in 1999, more than 1800 CubeSats have been launched into space. CubeSats often use commercial off-the-shelf components for their electronics and structure, and thus provide affordable access to space for the science community. Many major universities, elementary schools, private firms and organizations now have their own space programs. CubeSats have been widely used in missions such as Earth remote sensing, telecommunication demonstration, astronomical observation and biological research.

This Special Issue aims to publish studies covering the applications and technology of CubeSats. Topics may cover the results of past missions, the design of future missions or the technology related to CubeSat development. We would like to invite you to submit articles about your recent research on topics including but not limited to those listed below (review articles covering one or more of these topics are also very welcome).

  1. Applications of CubeSats to scientific research and civil use, especially to remote sensing.
  2. Technological development, including structure, propulsion, telecommunication, control, etc.
  3. Novel sensors on CubeSats.
  4. Onboard data processing techniques (target detection, image classification, sematic segmentation, etc.).
  5. Distributed system of CubeSats.
  6. CubeSats missions and technologies for deep space exploration.

Prof. Dr. Zhaokui Wang
Prof. Dr. Xiaozhou Yu
Dr. Lin Zhang
Dr. Farid Gamgami
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

  • CubeSat
  • nanosatellite
  • space missions
  • intelligent control
  • onboard image processing
  • planetary exploration
  • distributed space systems
  • trajectory optimization

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

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Research

19 pages, 5545 KiB  
Article
Rapid Detection and Orbital Parameters’ Determination for Fast-Approaching Non-Cooperative Target to the Space Station Based on Fly-around Nano-Satellite
by Chong Sun, Yongqing Sun, Xiaozhou Yu and Qun Fang
Remote Sens. 2023, 15(5), 1213; https://doi.org/10.3390/rs15051213 - 22 Feb 2023
Cited by 3 | Viewed by 1824
Abstract
Non-cooperative targets, such as space debris, defunct spacecrafts and LEO constellation satellites, have brought serious risks to the space station. The rapid detection and orbital parameters’ determination of the fast-approaching non-cooperative target can greatly improve the protection ability of the space station. In [...] Read more.
Non-cooperative targets, such as space debris, defunct spacecrafts and LEO constellation satellites, have brought serious risks to the space station. The rapid detection and orbital parameters’ determination of the fast-approaching non-cooperative target can greatly improve the protection ability of the space station. In this paper, a novel rapid detection and orbital parameters’ determination method based on the collaborative observation of the space station, and a fly-around nano-satellite is developed. The early-warning region of the space station is established, and considering the observation constraints of the nano-satellite, the non-cooperative target detection strategy is provided, which includes a collaborative observation configuration as well as the attitude variation of the cameras, and the detection efficiency is analyzed. Then, the orbital parameters’ filtering model of the non-cooperative target based on the collaborative observation is constructed, and the Unscented Kalman filter method is utilized to determinate the orbital parameters of the non-cooperative target. Considered the observability of the initial collaborative observation configuration, this paper analyzes the observation configuration with low observability in different scenarios, and proposes an optimal orbital maneuver algorithm for the nano-satellite. This algorithm can realize a fuel-optimal orbital maneuver that satisfies the minimum line-of-sight angle constraint of the collaborative observation. Full article
(This article belongs to the Special Issue CubeSats Applications and Technology)
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18 pages, 3900 KiB  
Article
Learning Lightweight and Superior Detectors with Feature Distillation for Onboard Remote Sensing Object Detection
by Lingyun Gu, Qingyun Fang, Zhaokui Wang, Eugene Popov and Ge Dong
Remote Sens. 2023, 15(2), 370; https://doi.org/10.3390/rs15020370 - 7 Jan 2023
Cited by 9 | Viewed by 2490
Abstract
CubeSats provide a low-cost, convenient, and effective way of acquiring remote sensing data, and have great potential for remote sensing object detection. Although deep learning-based models have achieved excellent performance in object detection, they suffer from the problem of numerous parameters, making them [...] Read more.
CubeSats provide a low-cost, convenient, and effective way of acquiring remote sensing data, and have great potential for remote sensing object detection. Although deep learning-based models have achieved excellent performance in object detection, they suffer from the problem of numerous parameters, making them difficult to deploy on CubeSats with limited memory and computational power. Existing approaches attempt to prune redundant parameters, but this inevitably causes a degradation in detection accuracy. In this paper, the novel Context-aware Dense Feature Distillation (CDFD) is proposed, guiding a small student network to integrate features extracted from multi-teacher networks to train a lightweight and superior detector for onboard remote sensing object detection. Specifically, a Contextual Feature Generation Module (CFGM) is designed to rebuild the non-local relationships between different pixels and transfer them from teacher to student, thus guiding students to extract rich contextual features to assist in remote sensing object detection. In addition, an Adaptive Dense Multi-teacher Distillation (ADMD) strategy is proposed, which performs adaptive weighted loss fusion of students with multiple well-trained teachers, guiding students to integrate the learning of helpful knowledge from multiple teachers. Extensive experiments were conducted on two large-scale remote sensing object detection datasets with various network structures; the results demonstrate that the trained lightweight network achieves auspicious performance. Our approach also shows good generality for existing state-of-the-art remote sensing object detectors. Furthermore, by experimenting on large general object datasets, we demonstrate that our approach is equally practical for general object detection distillation. Full article
(This article belongs to the Special Issue CubeSats Applications and Technology)
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31 pages, 7102 KiB  
Article
Self-Organizing Control of Mega Constellations for Continuous Earth Observation
by Yun Xu, Yulin Zhang, Zhaokui Wang, Yunhan He and Li Fan
Remote Sens. 2022, 14(22), 5896; https://doi.org/10.3390/rs14225896 - 21 Nov 2022
Cited by 6 | Viewed by 2337
Abstract
This work presents a novel self-organizing control method for mega constellations to meet the continuous Earth observation requirements. In order to decrease the TT&C pressure caused by numerous satellites, constellation satellites are not controlled according to the designed configurations but are controlled with [...] Read more.
This work presents a novel self-organizing control method for mega constellations to meet the continuous Earth observation requirements. In order to decrease the TT&C pressure caused by numerous satellites, constellation satellites are not controlled according to the designed configurations but are controlled with respect to intersatellite constraints. By analyzing the street-of-coverage (SOC) of coplanar constellation satellites, the continuous coverage constraint of the mega constellation is transformed into constraints of the right ascension of ascending node (RAAN) and relative motion bound between every two adjacent coplanar satellites. The proposed continuous coverage constraint can be satisfied by most ongoing or planned mega constellations. Artificial potential functions (APFs) are used to realize self-organizing control. The scale-independent relative orbital elements (SIROEs) are innovatively presented as the self-organizing control variables. Using the Gaussian equations and Lyapunov’s theory, the stability of the APF control in quadratic form is proven, from which it can be concluded that the APF control variables of the controlled satellite should have the same time derivative as the target satellite states under two-body Keplerian motion condition, and SIROEs are ideal choices. The proposed controllers and self-organizing rules are verified in the sub-constellation of the GW-2 mega constellation by simulation. The results demonstrate the goodness in control effect and ground coverage performance. Full article
(This article belongs to the Special Issue CubeSats Applications and Technology)
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24 pages, 18951 KiB  
Article
A Robust Star Identification Algorithm Based on a Masked Distance Map
by Hao Yuan, Dongxu Li and Jie Wang
Remote Sens. 2022, 14(19), 4699; https://doi.org/10.3390/rs14194699 - 21 Sep 2022
Cited by 5 | Viewed by 1963
Abstract
The authors of this paper propose a robust star identification algorithm for a ‘Lost-In-Space’-mode star tracker for lost-cost CubeSat missions. A two-step identification framework and an embedded validation mechanism were designed to accelerate the process. In the first step, a masked distance map [...] Read more.
The authors of this paper propose a robust star identification algorithm for a ‘Lost-In-Space’-mode star tracker for lost-cost CubeSat missions. A two-step identification framework and an embedded validation mechanism were designed to accelerate the process. In the first step, a masked distance map is designed to provide a shortlist of stars, and the embedded fast validation process enables the direct output of validated stars before the second step. In the second step, local similarity is utilized to select a set of stars from those shortlisted, and the final validation procedure rejects all unsatisfactory stars. This algorithm can provide reliable and robust recognition even when the captured star images include severe star positioning errors, missing stars and false stars. The proposed algorithm was verified by a simulation study under various conditions. As low-cost star sensors face harsh and unknown environments during deep space CubeSat missions such as asteroid exploration, the proposed algorithm with high robustness will provide an important function. Full article
(This article belongs to the Special Issue CubeSats Applications and Technology)
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20 pages, 14579 KiB  
Article
Design and Analysis of a New Deployer for the in Orbit Release of Multiple Stacked CubeSats
by Yong Zhao, Honghao Yue, Xingke Mu, Xiaoze Yang and Fei Yang
Remote Sens. 2022, 14(17), 4205; https://doi.org/10.3390/rs14174205 - 26 Aug 2022
Cited by 3 | Viewed by 2385
Abstract
More and more CubeSats cooperate to implement complex space exploration missions. In order to store and deploy more CubeSats in a rocket-launch mission, this paper presents a new CubeSat deployer with large-capacity storage. Different from the traditional one with the compression springs, the [...] Read more.
More and more CubeSats cooperate to implement complex space exploration missions. In order to store and deploy more CubeSats in a rocket-launch mission, this paper presents a new CubeSat deployer with large-capacity storage. Different from the traditional one with the compression springs, the deployer with electromagnetic actuators is proposed to achieve the transportation and release. A new electromagnetic actuator with high thrust density was applied to adjust the release speeds of the CubeSats with different masses, and a new electromagnetic convey platform with attractive force was designed to transfer the stacked CubeSats to the release window. The equivalent magnetic circuit method was used to the establish electromagnetic force models. The simplified dynamic models of the transportation and release were built. The magnetic field, electromagnetic force, and motion characteristics were analyzed. The prototype was developed to verify the performance of the proposed configuration of the deployer with electromagnetic actuators. The experimental results show that stacked CubeSats can be transported smoothly even under constant external interference. The launcher achieved high thrust density and effectively adjusted the separation speed of the CubeSats. Full article
(This article belongs to the Special Issue CubeSats Applications and Technology)
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19 pages, 920 KiB  
Article
Image-Based Adaptive Staring Attitude Control for Multiple Ground Targets Using a Miniaturized Video Satellite
by Chao Song, Caizhi Fan and Mengmeng Wang
Remote Sens. 2022, 14(16), 3974; https://doi.org/10.3390/rs14163974 - 16 Aug 2022
Cited by 2 | Viewed by 1781
Abstract
A miniaturized video satellite can observe the ground targets by recording real-time video clips in staring control mode and therefore obtains a unique advantage over traditional remote sensing techniques. To further extend the application of a video satellite, a strategy for simultaneously observing [...] Read more.
A miniaturized video satellite can observe the ground targets by recording real-time video clips in staring control mode and therefore obtains a unique advantage over traditional remote sensing techniques. To further extend the application of a video satellite, a strategy for simultaneously observing a group of ground targets is to be developed. To cope with the impacts of an uncalibrated camera on the pointing accuracy which can lead to the failure of a multi-target observation task, an adaptive attitude control method is to be exploited. Hence, to observe multiple ground targets using an onboard uncalibrated camera, this paper proposes an image-based adaptive staring attitude controller. First, a target-selection strategy is proposed to realize a more balanced staring observation of the target group. Second, an updating law is proposed to estimate the camera parameters according to the projection equations. At last, an adaptive staring controller based on the estimated parameters is formulated, so that the center of mass of the ground targets on the image can be controlled towards its desired location, which is normally the image center. The stability of the proposed staring controller is proved using Barbalat’s Lemma. The simulation results show that even though the camera parameters are uncertain, the adaptive control method effectively achieves the staring observation for multiple ground targets by keeping their midpoint at the image center. Full article
(This article belongs to the Special Issue CubeSats Applications and Technology)
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