Accelerating Minimum Entropy Autofocus With Stochastic Gradient for UAV SAR Imagery

被引：9

作者：

Meng, Zhichao ^{[1
]}

Zhang, Lei ^{[1
]}

Ma, Yan ^{[2
]}

Wang, Guanyong ^{[3
]}

Jiang, Hejun ^{[4
]}

机构：

[1] Sun Yat Sen Univ, Sch Elect & Commun Engn, Shenzhen 510275, Peoples R China

[2] Beijing Inst Tracking Telemetry & Telecommun, Beijing 100094, Peoples R China

[3] Beijing Inst Radio Measurement, Beijing 100854, Peoples R China

[4] Sci & Technol Near Surface Detect Lab, Wuxi 214035, Jiangsu, Peoples R China

来源：

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2022年 / 19卷

基金：

中国国家自然科学基金;

关键词：

Radar polarimetry; Synthetic aperture radar; Entropy; Unmanned aerial vehicles; Error analysis; Azimuth; Estimation; Minimum entropy autofocusing (MEA); stochastic gradient (SG); synthetic aperture radar (SAR); unmanned aerial vehicle (UAV); MINIMIZATION;

D O I：

10.1109/LGRS.2021.3106636

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

Minimum entropy autofocus (MEA) has been applied in unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) imagery for its robustness in different circumstances. However, large amount of range cell samples to calculate the gradient for the minimum entropy optimization keeps its optimal convergence, which usually degrades the efficiency in real UAV SAR applications. In this letter, accelerated minimum entropy autofocus is proposed, which leverages both high computational efficiency and phase error estimation precision simultaneously. A strategy of stochastic gradient (SG) calculation is introduced in the MEA optimization with randomly selecting samples in each iteration through a probability distribution function (PDF). Experimental results with real UAV SAR data have validated the superior performance of the proposed SG-MEA algorithm.

引用

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