2016
DOI: 10.1049/iet-spr.2015.0162
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Two‐dimensional autofocus technique for high‐resolution spotlight synthetic aperture radar

Abstract: Motion errors are inevitably introduced when data is acquired and considerably degrade the image quality in terms of geometric resolution, radiometric accuracy and image contrast, especially in high-resolution spotlight synthetic aperture radar (SAR) imagery. In this study, the authors present a novel two-dimensional (2D) autofocus algorithm directly inserted into polar format algorithm, which compensates the envelop error and the phase error sequentially. A coarse error correction is first performed by global… Show more

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Cited by 19 publications
(4 citation statements)
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References 20 publications
(24 reference statements)
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“…To deal with the residual RCM, we manage to adjust the range resolution to a coarse one so that the residual RCM is within one range resolution. With iterative RCM correction scheme proceeding, the estimation of and become more and more accurate, and the coarse resolution can be adjusted to a finer one gradually [ 38 , 39 ]. The coarse-to-fine range resolution can be implemented as where is a constant, which stands for the range resolution amplification factor; is the changing rate of range resolution; the range resolution of the k th iteration is denoted by ; and is the range resolution of the original SAR data.…”
Section: Motion Modelling and Optimisationmentioning
confidence: 99%
“…To deal with the residual RCM, we manage to adjust the range resolution to a coarse one so that the residual RCM is within one range resolution. With iterative RCM correction scheme proceeding, the estimation of and become more and more accurate, and the coarse resolution can be adjusted to a finer one gradually [ 38 , 39 ]. The coarse-to-fine range resolution can be implemented as where is a constant, which stands for the range resolution amplification factor; is the changing rate of range resolution; the range resolution of the k th iteration is denoted by ; and is the range resolution of the original SAR data.…”
Section: Motion Modelling and Optimisationmentioning
confidence: 99%
“…In addition, there is an autofocus algorithm based on image optimization, which uses specific criteria to search a set of optimal parameters for the motion error model according to different actual requirements to achieve the best image quality. The criteria mainly include contrast optimization (CO) [12], sharpness [13][14][15], and minimum entropy (ME) [16][17][18]. Since there is no analytical mapping relationship between image quality and model parameters, the iterative search process is needed to meet the constraints in image optimization methods.…”
Section: Introductionmentioning
confidence: 99%
“…One is to estimate the 2-D phase error in a blind manner. They assume that the 2-D phase error is completely unknown and estimate all the 2-D phase error parameters directly [26]- [31]. Because of the large number of unknown phase parameters, these strategies often suffer from inferior computational efficiency and parameter estimate accuracy.…”
Section: Introductionmentioning
confidence: 99%