Space object identification based on narrowband radar cross section

Cai, Wei; Fu, Xiongjun; Zhang, Qian; Jiao, Liudan; Gao, Meiguo

doi:10.1109/cisp.2012.6469825

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…RCS, as one crucial physical characteristic of radar target, provides information about the type of the SO. As a result, radars capable of measuring the RSO's characteristics are helpful to catalogue the RSO based on RCS [6].…”

Section: Introductionmentioning

confidence: 99%

“…RCS data also have been widely utilized in previous work for the RSO classification and identification. Nevertheless, there are one tricky problem, an RSO's RCS data influenced by various factors, such as its shape, motion, angular velocity, atmospheric conditions, altitude, material, and the characteristics of the radar [9]. Resolving this issue is a challenge topic, because of its physical complexity, Cai Wang et al using the k-nearest neighbor (KNN) fuzzy classifier to accomplish the object identification [10].…”

Section: Introductionmentioning

confidence: 99%

“…Resolving this issue is a challenge topic, because of its physical complexity, Cai Wang et al using the k-nearest neighbor (KNN) fuzzy classifier to accomplish the object identification [10]. In Reference [9], statistical features, size features and altitude ability features were extracted from RCS sequences by Jin Sheng et al The Greedy algorithm was used by Xuehui Lei et al with the extracted statistical features [11]. Hence, applying machine learning methods on RCS in realworld may offer a confidential approach.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning with oversampling for space debris classification based on radar cross section

Zhang

2024

ACE

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Over the past few years, the likelihood of collision of space objects increases as the quantity of space debris rises. Space debris classification and identification becomes more crucial to space assets security and space situation awareness. Radar cross section (RCS), one of the essential arguments for tracking space debris, was measured by European Incoherent Scatter Scientific Association (EISCAT) and other radar systems. This study investigates the effectiveness of seven machine learning methods employed to address the classification of space objects based on RCS data from European Space Agency (ESA). To tackle the Class-imbalance issue in this study (the ratio of space debris to non-debris is approximately 5:1 in the dataset), three oversampling techniques are employed, including: Synthetic Minority Oversampling Technique (SMOTE), synthetic minority oversampling technique-support vector machine (SMOTE-SVM) and Adaptive Synthetic Sampling (ADASYN). The experiments show that, in the test set, the combination of SVM with SMOTE-SVM oversampling techniques can reach the accuracy of 99.7%, the precision of 98.7% and the recall of 99.4% which is better than the rest of models.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%