CHENG Di,
CHEN Chang,,
CHEN Weidong
School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China
Funds:The National Natural Science Foundation of China (61971392)
More Information
Author Bio:QU Haiyou received the B.S. degree in electronic engineering from Nanjing University of Science and Technology in 2019. He is currently pursuing the Ph.D. degrees in the School of Information Science and Technology, University of Science and Technology of China, Hefei, China. His research interests include vortex electromagnetic imaging, Bayesian model, optimization algorithms and signal processing
CHENG Di received B.S. degree in remote sensing science and technology from Harbin Institute of Technology, Harbin, China, in 2018. He is currently pursuing the Ph.D. degrees in the School of Information Science and Technology, University of Science and Technology of China, Hefei, China. His research interests include signal processing, radar imaging, optimization algorithms and target tracking
CHEN Chang received B.S., M.S. and Ph.D. degrees in University of Science and Technology of China (USTC), Hefei, China, in 2002, 2005 and 2012 respectively. He is an Associate Professor in the School of Information Science and Technology, USTC, China. He has considerable experience in antenna design, microwave imaging, and microwave passive components. He is currently interested in microwave antennas, microwave metamaterials, and electromagnetic spectrum sensing
CHEN Weidong received the B.S. degree in electronic engineering from the University of Electronic Science and Technology of China, Chengdu, China, in 1990, and the M.Eng. and Ph.D. degrees in electromagnetic field and microwave technology from the University of Science and Technology of China (USTC), Hefei, China, in 1994 and 2005, respectively. He serves as a Professor with the Department of Electronic Engineering, USTC. His research interests are in the areas of microwave imaging theory with applications to radar
Corresponding author:CHEN Chang, chench@mail.ustc.edu.cn
摘要
摘要:基于轨道角动量(OAM)的涡旋雷达因其在高分辨率成像方面具有巨大潜力而受到广泛关注。有限OAM模式下的涡旋雷达高分辨率成像问题,通常采用稀疏恢复的方法来解决,这种方法需要精确地已知成像模型的先验知识。然而,系统中不可避免存在的相位误差,会导致成像模型失配,严重影响成像性能。为了解决这一问题,该文首次建立了存在相位误差时的涡旋雷达成像模型。同时,提出了一种涡旋雷达两步自校正成像方法,用于直接估计相位误差。首先在第1步中提出了一种稀疏驱动算法来促进目标稀疏性,同时提升成像重构性能。其次,在第2步中提出了一种直接补偿相位误差的自校正操作。该方法通过对目标重构和相位误差估计的交替迭代,能够很好地重建目标并有效地补偿相位误差。仿真结果表明,该方法在提高成像质量和改善相位误差估计性能方面具有潜在的优势。
关键词:涡旋雷达成像/
轨道角动量/
相位误差/
自校正/
稀疏恢复
Abstract:The Orbital Angular Momentum (OAM)-based vortex radar has drawn increasing attention because of its potential for high-resolution imaging. The vortex radar high resolution imaging with limited OAM modes is commonly solved by sparse recovery, in which the prior knowledge of the imaging model needs to be known precisely. However, the inevitable phase error in the system results in imaging model mismatch and deteriorates the imaging performance considerably. To address this problem, the vortex radar imaging model with phase error is established for the first time in this work. Meanwhile, a two-step self-calibration imaging method for vortex radar is proposed to directly estimate the phase error. In the first step, a sparsity-driven algorithm is developed to promote sparsity and improve imaging performance. In the second step, a self-calibration operation is performed to directly compensate for the phase error. By alternately reconstructing the targets and estimating the phase error, the proposed method can reconstruct the target with high imaging quality and effectively compensate for the phase error. Simulation results demonstrate the advantages of the proposed method in enhancing the imaging quality and improving the phase error estimation performance.
Key words:Vortex radar imaging/
Orbital Angular Momentum (OAM)/
Phase error/
Self-calibration/
Sparse recovery
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https://plugin.sowise.cn/viewpdf/198_f22270c4-38dc-4583-b882-388215af09e0_R21094