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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Performance Analysis of Autofocus Algorithms for Compensating Ionospheric Dispersion Effect on Spaceborne Low-Frequency SAR Focusing
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Performance Analysis of Autofocus Algorithms for Compensating Ionospheric Dispersion Effect on Spaceborne Low-Frequency SAR Focusing

机译：用于补偿离子层分散效应的自动对焦算法对空间低频SAR聚焦的性能分析

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摘要

Spaceborne low-frequency synthetic aperture radar with high resolution will be affected by ionospheric dispersion, which introduces a quadratic range phase error, leading to range defocusing. The dependence of the performance of contrast optimization (CO) autofocus and multilook registration (MLR) autofocus for dispersion compensation on the signal-to-clutter ratio (SCR) is analyzed. For SCR >20 dB, two autofocus algorithms have similar performances, and CO is a little better. For SCR < 20 dB, CO, and MLR performance will be affected by initial quadratic phase error (QPE) and clutter size, respectively. When the initial QPE is less than 530°, and clutter size is at least 100 resolution cells, CO performance will be better than MLR. Taking 45° QPE as the threshold for accurate focusing, in the case of the single point object, the threshold SCR for CO to be effective is approximately between 12.5 and 18 dB, corresponding to initial QPE between 0° and 800°. For MLR, the threshold SCR is approximately between 17.5 and 18.5 dB, corresponding to clutter size between 100 and 500 resolution cells. For the case of multiple point objects, the threshold SCR values of both algorithms will decrease. Simulations based on scenes derived from PALSAR2 data demonstrate the effectiveness of the CO and MLR autofocus algorithm.
机译：具有高分辨率的空间播种低频合成孔径雷达将受电离层分散的影响，这引入了二次范围相位误差，导致范围散焦。对比度优化（CO）自动对比度和多核登记（MLR）自动对焦对信号 - 杂波比（SCR）进行分散补偿的依赖性。对于SCR> 20 dB，两个自动对焦算法具有相似的性能，CO有点好。对于SCR <20 dB，CO和MLR性能，分别受初始二次相位误差（QPE）和杂波大小的影响。当初始QPE小于530°时，杂波尺寸至少为100分辨率的细胞，CO性能将优于MLR。采用45°QPE作为精确聚焦的阈值，在单点对象的情况下，用于有效的阈值SCR大约在12.5和18dB之间，对应于0°和800°之间的初始QPE。对于MLR，阈值SCR大约在17.5和18.5dB之间，对应于100和500分辨率之间的杂波尺寸。对于多个点对象的情况，两个算法的阈值SCR值将减小。基于来自Palsar2数据的场景模拟，证明了CO和MLR自动对焦算法的有效性。

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