高斜视聚光SAR的波数域二维可分离数据重构算法

IF 4.2 2区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Computational Imaging Pub Date : 2025-03-12 DOI:10.1109/TCI.2025.3551164

Qianyu Deng;Yan Jiang;Xinhua Mao

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引用次数: 0

摘要

在高斜视聚光合成孔径雷达（SAR）中，如果采用固定的接收窗，信号在时域和频域都会发生畸变，导致大量数据冗余。为了提高采样效率，本文采用滑动接收窗进行信号采样。然而，使用滑动接收窗口引入二维耦合，需要二维插值进行解耦。为了实现高效准确的解耦，提出了一种波数域二维可分离数据重构算法，将二维插值简化为两个可分离的一维插值。本文提出的新算法不仅可以解决高斜视模式下二维频谱失真导致的频域采样效率低的问题，而且可以提高滑动接收窗模式下消除二维耦合的处理效率。通过点目标仿真和实际数据处理验证了该算法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Wavenumber Domain 2-D Separable Data Reformatting Algorithm for High Squint Spotlight SAR

In the case of high squint spotlight synthetic aperture radar (SAR), if a fixed receive-window is used, the signal is distorted in time domain and frequency domain, leading to a significant amount of redundant data. To improve sampling efficiency, this paper adopts a sliding receive-window for signal sampling. However, using a sliding receive-window introduces 2-D coupling, necessitating 2-D interpolation for decoupling. To achieve efficient and accurate decoupling, this paper proposes a wavenumber domain 2-D separable data reformatting algorithm, which simplifies the 2-D interpolation into two separable 1-D interpolations. The new algorithm proposed in this paper can not only solve the problem of low sampling efficiency in the frequency domain caused by the distortion of the 2-D spectrum in high squint mode, but also improve the processing efficiency of eliminating the 2-D coupling in sliding receive-window mode. The effectiveness of the proposed algorithm is verified by point target simulations and real data processing.

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来源期刊

IEEE Transactions on Computational Imaging Mathematics-Computational Mathematics

CiteScore

8.20

自引率

7.40%

发文量

期刊介绍： The IEEE Transactions on Computational Imaging will publish articles where computation plays an integral role in the image formation process. Papers will cover all areas of computational imaging ranging from fundamental theoretical methods to the latest innovative computational imaging system designs. Topics of interest will include advanced algorithms and mathematical techniques, model-based data inversion, methods for image and signal recovery from sparse and incomplete data, techniques for non-traditional sensing of image data, methods for dynamic information acquisition and extraction from imaging sensors, software and hardware for efficient computation in imaging systems, and highly novel imaging system design.