欠采样
方位角
计算机科学
合成孔径雷达
压缩传感
采样(信号处理)
奈奎斯特-香农抽样定理
奈奎斯特率
计算机视觉
算法
人工智能
模式识别(心理学)
滤波器(信号处理)
数学
几何学
作者
Yitian Wu,Zhe Zhang,Xiaolan Qiu,Yao Zhao,Zhe Zhang
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:62: 1-18
标识
DOI:10.1109/tgrs.2024.3397826
摘要
Breaking the constraint of pulse repetition frequency (PRF) is one of the important development trends of synthetic aperture radar (SAR). Within the conventional azimuth sampling patterns, severe ambiguity arises when confronted at a low PRF. Conversely, elevated PRF introduces considerable data redundancy, thereby culminating in wasting of resources. To address these issues, this paper proposes a novel joint optimization network for sparse SAR imaging and azimuth undersampling pattern grounded in the model-based reconstruction using deep learned priors (MoDL) architecture, combined with matched filter (MF) approximate measurement operators, named MF-based sampling pattern Joint optimization MoDL sparse SAR imaging Network (MF-JMoDL-Net). The MF-JMoDL-Net incorporates non-uniform sampling operators, enabling the sampling positions to be learnable, and achieves the groundbreaking joint optimization of the sampling pattern and ambiguity suppression. When the PRF is below the Nyquist sampling rate, the proposed network can acquire SAR images with minimal ambiguity and optimal imaging quality. Furthermore, the final learned undersampling pattern can be visualized and combined with the SAR echo signal semantics for mutual feedback. Extensive experiments on simulated and real scenes datasets are conducted to demonstrate the effectiveness and superiority of the proposed framework in imaging results.
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