Analysis of the optical scattering characteristics of different types of space targets

Han, Yi; Sun, Heming; Feng, Jianguang; Li, Liang

doi:10.1088/0957-0233/25/7/075203

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…Models describing characteristics of RSO have been studied quite extensively. There are many models that specify the characteristics of different types of RSO from different perspectives, such as models of space object (Han et al 2014 ; Henderson 2014 ; Howard et al 2015 ; Poole and Murray-Krezan 2015 ) and debris (Rovetto 2016 ), material density distribution model of space debris (Opiela 2009 ). The distinct characteristics of a specific type of RSO can be used as internal knowledge of RSO to distinguish the type of RSO from others, for example, photometric signatures can be used to classify rocket body and communication satellite (Howard et al 2015 ).…”

Section: Related Workmentioning

confidence: 99%

Harnessing ontology and machine learning for RSO classification

2016

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Classification is an important part of resident space objects (RSOs) identification, which is a main focus of space situational awareness. Owing to the absence of some features caused by the limited and uncertain observations, RSO classification remains a difficult task. In this paper, an ontology for RSO classification named OntoStar is built upon domain knowledge and machine learning rules. Then data describing RSO are represented by OntoStar. A demo shows how an RSO is classified based on OntoStar. It is also shown in the demo that traceable and comprehensible reasons for the classification can be given, hence the classification can be checked and validated. Experiments on WEKA show that ontology-based classification gains a relatively high accuracy and precision for classifying RSOs. When classifying RSOs with imperfect data, ontology-based classification keeps its performances, showing evident advantages over classical machine learning classifiers who either have increases of 5 % at least in FP rate or have decreases of 5 % at least in indexes such as accuracy, precision and recall.

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Section: Related Workmentioning

confidence: 99%

Harnessing ontology and machine learning for RSO classification

2016

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“…The brightness condition refers to the intensity of light emitted or reflected by objects in space. Different objects can have different brightness levels, [8]. This aspect is particularly critical for optical sensors and telescopes.…”

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confidence: 99%

Deep learning‐based space debris detection for space situational awareness: A feasibility study applied to the radar processing

Massimi,

Ferrara,

Petrucci

et al. 2024

IET Radar Sonar & Navi

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The increasing number of space objects (SO), debris, and constellation of satellites in Low Earth Orbit poses a significant threat to the sustainability and safety of space operations, which must be carefully and efficiently addressed to avoid mutual collisions. The space situational awareness is currently addressed by an ensemble of radar and radio‐telescopes that detect and track SO. However, a large part of space debris is composed of very small and tiny metallic objects, very difficult to detect. The authors demonstrate the benefits of using deep learning (DL) architectures for small space object detection by radar observations. TIRA radio telescope has been simulated to generate range‐Doppler maps, then used as inputs for object detection exploiting You‐Only‐Look‐Once (YOLO) frameworks. The results demonstrate that the object detection by using YOLO algorithms outperform conventional target detection approaches, thus indicating the potential benefits of using DL techniques for space surveillance applications.

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Method for Fine Pattern Recognition of Space Targets Using the Entropy Weight Fuzzy-Rough Nearest Neighbor Algorithm

Yuan²,

Li³

2021

J Appl Spectrosc

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Analysis of the optical scattering characteristics of different types of space targets

Cited by 6 publications

References 11 publications

Harnessing ontology and machine learning for RSO classification

Harnessing ontology and machine learning for RSO classification

Deep learning‐based space debris detection for space situational awareness: A feasibility study applied to the radar processing

Method for Fine Pattern Recognition of Space Targets Using the Entropy Weight Fuzzy-Rough Nearest Neighbor Algorithm

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