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Angular Superresolution of Real Aperture Radar With High-Dimensional Data: Normalized Projection Array Model and Adaptive Reconstruction
IEEE Transactions on Geoscience and Remote Sensing ( IF 8.2 ) Pub Date : 2022-08-31 , DOI: 10.1109/tgrs.2022.3203131
Deqing Mao, Jianyu Yang, Yongchao Zhang, Weibo Huo, Fanyun Xu, Jifang Pei, Yin Zhang, Yulin Huang

Angular resolution of real aperture radar (RAR) can be improved using deconvolution methods to achieve enhanced target information based on the convolution relationship between target scatterings and an antenna pattern. However, depending on the wide scanning scope and dense sampling angular interval, the computational complexity of the deconvolution methods will drastically increase as the dimension of azimuthal data increases. In this article, to efficiently improve the angular resolution of RAR, a generalized adaptive asymptotic minimum variance (GAAMV) estimator that relies on a normalized projection array (NPA) model is proposed. On the one hand, the traditional convolution model of RAR is transformed into an NPA model to compress the data dimension. The proposed NPA model can normalize the signal model to make it independent of the sampling parameters. On the other hand, based on the NPA model, a GAAMV estimator is proposed to efficiently reconstruct the targets by adaptively updating each grid. Moreover, the penalty parameter is extended as a generalized case to improve its adaptability to different scenes. Based on the proposed model and method, the computational complexity can be decreased, especially for high-dimensional azimuthal data. Simulations and experimental data verify the proposed model and method.

中文翻译:

具有高维数据的真实孔径雷达的角超分辨率:归一化投影阵列模型和自适应重建

基于目标散射和天线方向图之间的卷积关系,可以使用反卷积方法提高真实孔径雷达(RAR)的角分辨率,以实现增强的目标信息。然而,取决于宽扫描范围和密集采样角间隔,反卷积方法的计算复杂度将随着方位角数据维数的增加而急剧增加。在本文中,为了有效提高 RAR 的角分辨率,提出了一种基于归一化投影阵列 (NPA) 模型的广义自适应渐近最小方差 (GAAMV) 估计器。一方面,将RAR的传统卷积模型转化为NPA模型来压缩数据维度。提出的 NPA 模型可以对信号模型进行归一化,使其独立于采样参数。另一方面,基于 NPA 模型,提出了一种 GAAMV 估计器,通过自适应更新每个网格来有效地重建目标。此外,惩罚参数被扩展为一个广义的案例,以提高其对不同场景的适应性。基于所提出的模型和方法,可以降低计算复杂度,特别是对于高维方位角数据。仿真和实验数据验证了所提出的模型和方法。基于所提出的模型和方法,可以降低计算复杂度,特别是对于高维方位角数据。仿真和实验数据验证了所提出的模型和方法。基于所提出的模型和方法,可以降低计算复杂度,特别是对于高维方位角数据。仿真和实验数据验证了所提出的模型和方法。
更新日期:2022-08-31
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