Extended Polar Format Algorithm for Non-Planar Target Imaging With DSM

IF 4.2 2区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Computational Imaging Pub Date : 2024-10-31 DOI:10.1109/TCI.2024.3490382
Jingwei Chen;Daoxiang An;Dong Feng;Wu Wang;Zhimin Zhou
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Abstract

In case of circular or non-linear acquisition trajectory, synthetic aperture radar (SAR) focusing becomes increasingly sensitive to elevation. For non-planar target imaging, it not only appears fore-shortening but also blurred. As the wider integration angle and higher elevation of objects, the defocus cannot be ignored. Generally, the polar format algorithm (PFA) is an efficient imaging algorithm for circular or non-linear SAR. However, in the process of PFA, the impact of focusing at an incorrect altitude has not been considered. In this article, the conventional PFA is adapted to incorporate the known digital surface model (DSM) into the imaging process. Firstly, the maximum allowable elevation deviation (MAED) $\delta {{z}_{\max }}$ is derived. Secondly, for non-planar targets that are higher than $\delta {{z}_{\max }}$ , data extraction is applied in the range-Doppler domain. Additionally, a compensation function is multiplied, which is constructed based on DSM data separately. The corresponding original echo data is then replaced with the processed data. The whole method only involves fast Fourier transform (FFT) and complex multiplication which enhances operational efficiency. The simulated and experimental data results demonstrated the effectiveness and practicability of the proposed algorithm.
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利用 DSM 进行非平面目标成像的扩展极点格式算法
在圆形或非线性采集轨迹的情况下,合成孔径雷达(SAR)聚焦对仰角越来越敏感。对于非平面目标成像,不仅会出现前缩短,还会出现模糊。当物体的积分角越宽,仰角越高,散焦现象就越不容忽视。一般来说,极化格式算法(PFA)是一种高效的圆形或非线性合成孔径雷达成像算法。然而,在 PFA 的过程中,没有考虑到在错误高度聚焦的影响。本文对传统的 PFA 进行了调整,将已知的数字表面模型(DSM)纳入成像过程。首先,推导出最大允许仰角偏差(MAED)$\delta {{z}_{\max }}$。其次,对于高于 $\delta {{z}_{\max }}$ 的非平面目标,在测距-多普勒域中进行数据提取。此外,还要乘以一个补偿函数,该函数是根据 DSM 数据单独构建的。然后用处理后的数据替换相应的原始回波数据。整个方法只涉及快速傅立叶变换(FFT)和复数乘法,从而提高了运行效率。模拟和实验数据结果证明了所提算法的有效性和实用性。
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来源期刊
IEEE Transactions on Computational Imaging
IEEE Transactions on Computational Imaging Mathematics-Computational Mathematics
CiteScore
8.20
自引率
7.40%
发文量
59
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.
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