基于数据外推的扫描雷达角超分辨率场景边缘目标恢复

Deqing Mao, Yongchao Zhang, Yao Kang, Yin Zhang, Weibo Huo, Yulin Huang, Jianyu Yang
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引用次数: 0

摘要

雷达天线可以在扫描模式下工作,以获得大范围的观测。然而,对于位于场景边缘的目标,目标只被不到一半的雷达波束扫描。因此,使用传统的角度超分辨方法会导致场景边缘目标的畸变恢复。为了保证恢复目标在全场景下的性能,本文提出了一种基于数据外推的并行迭代自适应方法。首先,分析了场景边缘目标失真的原因。然后,用雷达波束的一半外推回波数据来补偿未观测数据。最后,提出了一种并行迭代自适应方法来有效地恢复目标。仿真数据验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scene Edge Target Recovery of Scanning Radar Angular Super-Resolution Based on Data Extrapolation
Radar antenna can work in scanning mode to obtain a wide region observation. However, for the targets located at the scene edge, the targets are only swept by less than half of the radar beam. Therefore, the scene edge targets are recovered distortedly using the conventional angular super-resolution methods. To keep the performance of recovered targets in the full scene, in this paper, a data extrapolation-based parallel iterative adaptive approach (PIAA) is proposed. First, we analyze the cause of scene edge target distortion. Then, the echo data is extrapolated by half of the radar beam to compensate the unobserved data. Last, a parallel iterative adaptive approach is proposed to recover the targets efficiently. Simulation data is applied to verify the proposed method.
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