利用空间机载次 THz 反合成孔径雷达识别轨道上的物体及其意图

IF 1.4 4区 管理学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Iet Radar Sonar and Navigation Pub Date : 2023-12-08 DOI:10.1049/rsn2.12513
Mikhail Cherniakov, Edward G. Hoare, Marina Gashinova, Emidio Marchetti, Andrew G. Stove
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引用次数: 0

摘要

空间态势感知的一个重要方面是估计空间物体的意图。本文讨论了如何从这些物体的反合成孔径雷达图像中获取判别特征,以及如何利用这些判别特征识别物体或估计其意图。例如,如果物体是已知类型的卫星,所提出的方案就能识别它。此外,还讨论了该方案检测物体受损情况的能力。重点是在亚太赫兹波段(通常为 300 千兆赫)获得的图像,因为在这些频率下观察到的漫散射具有更强的成像能力。本文还讨论了利用电磁模拟获得的图像来训练识别物体特征的子系统的重要性,并介绍了在这些极短波长下为大型物体创建这些模拟的实用方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Recognition of objects in orbit and their intentions with space-borne sub-THz Inverse Synthetic Aperture Radar

An important aspect of Space Situational Awareness is to estimate the intent of objects in space. This paper discusses how discriminating features can be obtained from Inverse Synthetic Aperture Radar images of such objects and how these discriminators can be used to recognise the objects or to estimate their intent. If the object is, for example, a satellite of a known type, the scheme proposed is able to recognise it. The ability of the scheme to detect damage to the object is also discussed. The focus is on imagery obtained in the sub-terahertz band (typically 300 GHz) because of the greater imaging capability given by the diffuse scattering which is observed at these frequencies. The paper also discusses the importance of being able to use images obtained by electromagnetic simulation to be able to train the subsystem which recognises features of the objects and describes a practical scheme for creating these simulations for large objects at these very short wavelengths.

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来源期刊
Iet Radar Sonar and Navigation
Iet Radar Sonar and Navigation 工程技术-电信学
CiteScore
4.10
自引率
11.80%
发文量
137
审稿时长
3.4 months
期刊介绍: IET Radar, Sonar & Navigation covers the theory and practice of systems and signals for radar, sonar, radiolocation, navigation, and surveillance purposes, in aerospace and terrestrial applications. Examples include advances in waveform design, clutter and detection, electronic warfare, adaptive array and superresolution methods, tracking algorithms, synthetic aperture, and target recognition techniques.
期刊最新文献
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