Sparse Targets Angular Super-resolution Reconstruction Method under Unknown Antenna Pattern Errors for Scanning Radar

被引：0
|
作者：
Zhang Y. [1 ,2 ]
Zhang P. [1 ]
Tuo X. [1 ]
Mao D. [1 ]
Zhang Y. [1 ,2 ]
Huang Y. [1 ]
Yang J. [1 ]
机构：
[1] School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu
[2] Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou
来源：
Journal of Radars | 2024年 / 13卷 / 03期
关键词：
Angular super-resolution; Scanning radar; Sparse reconstruction; Total least squares; Unknown antenna pattern errors;
D O I：
10.12000/JR23208
中图分类号：
学科分类号：
摘要：
Scanning radar angular super-resolution technology is based on the relationship between the target and antenna pattern, and a deconvolution method is used to obtain angular resolution capabilities beyond the real beam. Most current angular super-resolution methods are based on ideal distortion-free antenna patterns and do not consider pattern changes in the actual process due to the influence of factors such as radar radome, antenna measurement errors, and non-ideal platform motion. In practice, an antenna pattern often has unknown errors, which can result in reduced target resolution and even false target generation. To address this problem, this paper proposes an angular super-resolution imaging method for airborne radar with unknown antenna errors. First, based on the Total Least Square (TLS) criterion, this paper considers the effect of the pattern error matrix and derive the corresponding objective function. Second, this paper employs the iterative reweighted optimization method to solve the objective function by adopting an alternative iteration solution idea. Finally, an adaptive parameter update method is introduced for algorithm hyperparameter selection. The simulation and experimental results demonstrate that the proposed method can achieve super-resolution reconstruction even in the presence of unknown antenna errors, promoting the robustness of the superresolution algorithm. ©The Author(s) 2024.
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页码：646 / 666
页数：20
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