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Behavioural Characterisation of Resident Space Objects for Space Situational Awareness

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Satellite Missions for Earth and Planetary Exploration".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 13106

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Special Issue Editors

Dr. Yang Yang

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Guest Editor

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

Dr. Sanat K. Biswas

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Guest Editor

Department of Electronics and Communications Engineering, IIIT Delhi, New Delhi, India
Interests: GNSS; space navigation; space situational awareness

Dr. Xiaofeng Wu

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Guest Editor

School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, NSW 2006, Australia
Interests: space engineering; space manipulators
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The proliferation of space objects, i.e., the advent of small satellites and mega-constellation, poses significant challenges to space traffic management and highlights the need for a better understanding the behaviours of both operational satellites and nonoperational space debris in order to build up & maintain a catalogue and eliminate the risk of space collisions. Space tracking, target detection & identification, and state estimation are essential tasks for behavioural characterisation of such objects in the context of space situational awareness.

This Special Issue aims at addressing a wide spectrum of technical issues in space tracking, object detection, orbit/attitude determination, manoeuvring detection and estimation for residential space objects, which connect the fields of measurements, modelling, dynamics for space situational awareness applications. Topics may cover anything from ground tracking capabilities to space-based detection and estimation, from single orbit determination to multiple target tracking, etc.

Articles may address, but are not limited, to the following topics:

Space object detection from optical sensors
Initial orbit determination and orbit determination
Intelligent attitude determination and control
Data aggregation and fusion, e.g., angular/range/doppler measurements
Robust manoeuvre detection and estimation techniques
Space tracking and data correlation
Cislunar space situational awareness

Dr. Yang Yang
Dr. Sanat K. Biswas
Dr. Xiaofeng Wu
Guest Editors

Manuscript Submission Information

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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

astrodynamics
orbit determination
space object detection
manoeuvre detection and estimation
attitude determination
data fusion
space tracking

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Published Papers (7 papers)

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18 pages, 3087 KiB

Open AccessArticle

An Efficient Pose Estimation Algorithm for Non-Cooperative Space Objects Based on Dual-Channel Transformer

by Ruida Ye, Yuan Ren, Xiangyang Zhu, Yujing Wang, Mingyue Liu and Lifen Wang

Remote Sens. 2023, 15(22), 5278; https://doi.org/10.3390/rs15225278 - 7 Nov 2023

Cited by 1 | Viewed by 1649

Abstract

Non-cooperative space object pose estimation is a key technique for spatial on-orbit servicing, where pose estimation algorithms based on low-quality, low-power monocular sensors provide a practical solution for spaceborne applications. The current pose estimation methods for non-cooperative space objects using monocular vision generally consist of three stages: object detection, landmark regression, and perspective-n-point (PnP) solver. However, there are drawbacks, such as low detection efficiency and the need for prior knowledge. To solve the above problems, an end-to-end non-cooperative space object pose estimation learning algorithm based on dual-channel transformer is proposed, a feature extraction backbone network based on EfficientNet is established, and two pose estimation subnetworks based on transformer are also established. A quaternion SoftMax-like activation function is designed to improve the precision of orientation error estimating. The method only uses RGB images, eliminating the need for a CAD model of the satellite, and simplifying the detection process by using an end-to-end network to directly detect satellite pose information. Experiments are carried out on the SPEED dataset provided by the European Space Agency (ESA). The results show that the proposed algorithm can successfully predict the satellite pose information and effectively decouple the spatial translation information and orientation information, which significantly improves the recognition efficiency compared with other methods. Full article

(This article belongs to the Special Issue Behavioural Characterisation of Resident Space Objects for Space Situational Awareness)

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18 pages, 3313 KiB

Open AccessArticle

Improved Initial Orbit Determination Based on the Gooding Method of Low Earth Orbit Space Debris Using Space-Based Observations

by Yewen Yin, Zhenwei Li, Chengzhi Liu, Zhe Kang, Jiannan Sun and Long Chen

Remote Sens. 2023, 15(21), 5217; https://doi.org/10.3390/rs15215217 - 2 Nov 2023

Cited by 1 | Viewed by 1671

Abstract

Initial orbit determination (IOD), as a basis for initial orbit association and accurate orbit determination (OD), has a crucial role in the process of obtaining space debris orbit information. Among the traditional methods, the Gooding method has better convergence and stability. In this study, the Gooding method is enhanced to solve the issues discovered. A novel initial orbit determination (IOD) method is developed using the proposed improvement measures of the single-parameter initial value determination (SIVD) method, the fitted-curve noise suppression method, the restricted corrective value solution method, the removal of trivial solutions, etc. The experimental results verify the effectiveness of the improved method. The success rate of the initial orbit determination reached 99%, and the accuracy of the solved orbit parameters was significantly improved, especially the semi-major axis (SMA) error of less than 50 km, accounting for 88% of the total. It can be seen that the method meets the demand of space-based space debris cataloging for initial orbit association and can serve in the field of space situational awareness, which has important practical significance and application potential. Full article

(This article belongs to the Special Issue Behavioural Characterisation of Resident Space Objects for Space Situational Awareness)

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26 pages, 11097 KiB

Open AccessArticle

Orbital Uncertainty Propagation Based on Adaptive Gaussian Mixture Model under Generalized Equinoctial Orbital Elements

by Hui Xie, Tianru Xue, Wenjun Xu, Gaorui Liu, Haibin Sun and Shengli Sun

Remote Sens. 2023, 15(19), 4652; https://doi.org/10.3390/rs15194652 - 22 Sep 2023

Cited by 1 | Viewed by 1394

Abstract

The number of resident space objects (RSOs) has been steadily increasing over time, posing significant risks to the safe operation of on-orbit assets. The accurate prediction of potential collision events and implementation of effective and nonredundant avoidance maneuvers require the precise estimation of the orbit positions of objects of interest and propagation of their associated uncertainties. Previous research mainly focuses on striking a balance between accurate propagation and efficient computation. A recently proposed approach that integrates uncertainty propagation with different coordinate representations has the potential to achieve such a balance. This paper proposes combining the generalized equinoctial orbital elements (GEqOE) representation with an adaptive Gaussian mixture model (GMM) for uncertainty propagation. Specifically, we implement a reformulation for the orbital dynamics so that the underlying state and the moment feature of the GMM are propagated under the GEqOE coordinates. Starting from an initial Gaussian probability distribution function (PDF), the algorithm iteratively propagates the uncertainty distribution using a detection-splitting module. A differential entropy-based nonlinear detector and a splitting library are utilized to adjust the number of GMM components dynamically. Component splitting is triggered when a predefined threshold of differential entropy is violated, generating several GMM components. The final probability density function (PDF) is obtained by a weighted summation of the component distributions at the target time. Benefiting from the nonlinearity reduction caused by the GEqOE representation, the number of triggered events largely decreases, causing the necessary number of components to maintain uncertainty realism also to decrease, which enables the proposed approach to achieve good performance with much more efficiency. As demonstrated by the results of propagation in three scenarios with different degrees of complexity, compared with the Cartesian-based approach, the proposed approach achieves comparable accuracy to the Monte Carlo method while largely reducing the number of components generated during propagation. Our results confirm that a judicious choice of coordinate representation can significantly improve the performance of uncertainty propagation methods in terms of accuracy and computational efficiency. Full article

(This article belongs to the Special Issue Behavioural Characterisation of Resident Space Objects for Space Situational Awareness)

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20 pages, 5006 KiB

Open AccessEditor’s ChoiceArticle

An Intelligent Detection Method for Small and Weak Objects in Space

by Yuman Yuan, Hongyang Bai, Panfeng Wu, Hongwei Guo, Tianyu Deng and Weiwei Qin

Remote Sens. 2023, 15(12), 3169; https://doi.org/10.3390/rs15123169 - 18 Jun 2023

Cited by 4 | Viewed by 2223

Abstract

In the case of a boom in space resource development, space debris will increase dramatically and cause serious problems for the spacecraft in orbit. To address this problem, a novel context sensing-YOLOv5 (CS-YOLOv5) is proposed for small and weak space object detection, which could realize the extraction of local context information and the enhancement and fusion of spatial information. To enhance the expression ability of feature information and the identification ability of the network, we propose the cross-layer context fusion module (CCFM) through multiple branches in parallel to learn the context information of different scales. At the same time, to map the small-scale features sequentially to the features of the previous layer, we design the adaptive weighting module (AWM) to assist the CCFM in further enhancing the expression of features. Additionally, to solve the problem that the spatial information of small objects is easily lost, we designed the spatial information enhancement module (SIEM) to adaptively learn the weak spatial information of small objects that need to be protected. To further enhance the generalization ability of CS-YOLOv5, we propose a contrast mosaic data augmentation to enrich the diversity of the sample. Extensive experiments are conducted on self-built datasets, which strongly prove the effectiveness of our method in space object detection. Full article

(This article belongs to the Special Issue Behavioural Characterisation of Resident Space Objects for Space Situational Awareness)

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20 pages, 4244 KiB

Open AccessArticle

A Case Study on the Effect of Atmospheric Density Calibration on Orbit Predictions with Sparse Angular Data

by Junyu Chen, Jizhang Sang, Zhenwei Li and Chengzhi Liu

Remote Sens. 2023, 15(12), 3128; https://doi.org/10.3390/rs15123128 - 15 Jun 2023

Cited by 2 | Viewed by 1128

Abstract

Accurately modeling the density of atmospheric mass is critical for orbit determination and prediction of space objects. Existing atmospheric mass density models (ADMs) have an accuracy of about 15%. Developing high-precision ADMs is a long-term goal that requires a better understanding of atmospheric density characteristics, more accurate modeling methods, and improved spatiotemporal data. This study proposes a method for calibrating ADMs using sparse angular data of space objects in low-Earth orbit over a certain period of time. Applying the corrected ADM not only improves the accuracy of orbit determination, but also enhances the accuracy of orbit prediction beyond the correction period. The study compares the impact of two calibration methods: atmospheric mass density model coefficient (ADMC) calibration and high precision satellite drag model (HASDM) calibration on the accuracy of orbit prediction of space objects. One month of ground-based telescope array angular data is used to validate the results. Space objects are classified as calibration objects, participating in ADM calibration, and verification objects, inside and outside the calibration orbit region, respectively. The results show that applying the calibrated ADM can significantly increase the accuracy of orbit prediction. For objects within the calibration orbit region, the calibration object’s orbit prediction error was reduced by about 55%, while that of verification objects was reduced by about 45%. The reduction in orbit prediction error outside this region was about 30%. This proposed method contributes significantly to the development of more reliable ADMs for orbit prediction of space objects with sparse angular data and can provide significant academic value in the field of space situational awareness. Full article

(This article belongs to the Special Issue Behavioural Characterisation of Resident Space Objects for Space Situational Awareness)

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33 pages, 13162 KiB

Open AccessArticle

Dim and Small Space-Target Detection and Centroid Positioning Based on Motion Feature Learning

by Shengping Su, Wenlong Niu, Yanzhao Li, Chunxu Ren, Xiaodong Peng, Wei Zheng and Zhen Yang

Remote Sens. 2023, 15(9), 2455; https://doi.org/10.3390/rs15092455 - 7 May 2023

Cited by 4 | Viewed by 2424

Abstract

The detection of dim and small space-targets is crucial in space situational awareness missions; however, low signal-to-noise ratio (SNR) targets and complex backgrounds pose significant challenges to such detection. This paper proposes a space-target detection framework comprising a space-target detection network and a k-means clustering target centroid positioning method. The space-target detection network performs a three-dimensional convolution of an input star image sequence to learn the motion features of the target, reduces the interference of noise using a soft thresholding module, and outputs the target detection result after positioning via the offsetting branch. The k-means centroid positioning method enables further high-precision subpixel-level centroid positioning of the detection network output. Experiments were conducted using simulated data containing various dim and small space-targets, multiple noises, and complex backgrounds; semi-real data with simulated space-targets added to the real star image; and fully real data. Experiments on the simulated data demonstrate the superior detection performance of the proposed method for multiple SNR conditions (particularly with very low false alarm rates), robustness regarding targets of varying numbers and speeds, and complex backgrounds (such as those containing stray light and slow motion). Experiments performed with semi-real and real data both demonstrate the excellent detection performance of the proposed method and its generalization capability. Full article

(This article belongs to the Special Issue Behavioural Characterisation of Resident Space Objects for Space Situational Awareness)

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14 pages, 2559 KiB

Open AccessTechnical Note

New Space Object Cataloguing through Very-Short-Arc Data Mining

by Lei Liu, Bin Li, Jizhang Sang, Shengfu Xia and Xiangxu Lei

Remote Sens. 2023, 15(19), 4848; https://doi.org/10.3390/rs15194848 - 7 Oct 2023

Viewed by 1210

Abstract

The space surveillance network collects significant quantities of space object monitoring data on a daily basis, which varies in duration and contain observation errors. Cataloguing space objects based on these data may result in a large number of very short arcs (VSAs) being wasted due to cataloguing flaws, poor data quality, data precessing, and so on. To address this problem, an effective data mining method based on tracklet-to-object matching is proposed to improve the data utilization in new object cataloguing. The method can enhance orbital constraints based on useful track information in mined tracklets, improve the accuracy of catalogued orbits, and achieve the transformation of omitted observations into “treasures”. The performance of VSAs is evaluated in tracklet-to-object matching, which is less sensitive to tracklet duration and separation time than initial orbit determination (IOD) and track association. Further, the data mining method is applied to new space object cataloguing based on radar tracklets and achieved significant improvements. The 5-day data utilization increased by 9.5%, and the orbit determination and prediction accuracy increased by 11.1% and 23.6%, respectively, validating the effectiveness of our method in improving the accuracy of space object orbit cataloguing. The method shows promising potential for the space object cataloguing and relevant applications. Full article

(This article belongs to the Special Issue Behavioural Characterisation of Resident Space Objects for Space Situational Awareness)

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