Two‐dimensional autofocus technique for high‐resolution spotlight synthetic aperture radar

Zeng, Letian; Liang, Yi; Xing, Mengdao; Li, Zhenyu

doi:10.1049/iet-spr.2015.0162

Cited by 19 publications

(4 citation statements)

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%

Squinted Airborne Synthetic Aperture Radar Imaging with Unknown Curved Trajectory

Huang

et al. 2020

Sensors

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The imagery of airborne highly squinted synthetic aperture radar (SAR) with curved trajectory is a challenging task due to the translational-variant range cell migration (RCM) and azimuth modulation. However, in most cases of practical application, the curved trajectory cannot be accurately known, which brings greater difficulties to the imaging problem. To accommodate these issues, we propose a novel motion modelling and optimisation based imaging algorithm for the highly squinted SAR with unknown curved trajectory. First, to correct the translational-variant RCM, a coarse-to-fine RCM correction scheme as well as a range perturbation approach is applied. Afterwards, an optimisation model of motion information under the criterion of minimum entropy is built during the azimuth processing by nonlinear chirp scaling (NLCS). Correspondingly, a differential evolution (DE) optimisation strategy is proposed to estimate the motion information in an iterative manner. We empirically compare the proposed algorithms with several state-of-the-art highly squinted curved SAR imaging algorithms. Numerical results show the effectiveness of the proposed method in the case without any prior information of the curved trajectory.

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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%

Squinted Airborne Synthetic Aperture Radar Imaging with Unknown Curved Trajectory

Huang

et al. 2020

Sensors

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“…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%

A Modified Iteration-Free SPGA Based on Removing the Linear Phase

Xie,

Luan,

Chen

et al. 2023

Remote Sensing

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In traditional Stripmap SAR imaging, the platform motion error will bring the phase error in the azimuthal direction to the image, which will have a series of effects on the imaging quality. The traditional autofocus algorithm—Stripmap Phase Gradient Algorithm (SPGA)—can estimate any order phase error above the second order in theory, but it is difficult to estimate the linear phase error, which leads to the discontinuity of the estimated phase error. It usually needs multiple iterations to focus an image, which is inefficient. Moreover, because the linear phase error cannot be estimated, the traditional SPGA cannot eliminate the target offset in the image, resulting in the distortion of the image in the azimuthal direction. According to the continuity of phase error, we propose a modified iteration-free SPGA based on removing the linear phase. Without iteration, the proposed autofocus algorithm can achieve comparable or even better results than traditional SPGA. In the simulation experiments, piecewise linear errors are added to the images of multiple targets. SPGA still fails to focus the image after six iterations. The average ILSR and ILSR are −7.11 dB and −3.99 dB, respectively, and the average number of point target drift is 8.42 pixels. The proposed algorithm optimizes the average ILSR and ILSR to −12.34 dB and −9.87 dB and reduces the average number of point target drift to 0.16 pixels. In the actual data processing, using image entropy as the evaluation criterion, the time consumption is only 19.25% of SPGA under the condition of achieving the same focusing quality.

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“…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%

Knowledge-Aided 2-D Autofocus for Spotlight SAR Filtered Backprojection Imagery

Mao

Ding

Zhang

et al. 2019

IEEE Trans. Geosci. Remote Sensing

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Filtered backprojection (FBP) algorithm is a popular choice for complicated trajectory SAR image formation processing due to its inherent nonlinear motion compensation capability. However, how to efficiently autofocus the defocused FBP imagery when the motion measurement is not accurate enough is still a challenging problem. In this paper, a new interpretation of the FBP derivation is presented from the Fourier transform point of view. Based on this new viewpoint, the property of the residual 2-D phase error in FBP imagery is analyzed in detail. Then, by incorporating the derived a priori knowledge on the 2-D phase error, an accurate and efficient 2-D autofocus approach is proposed. The new approach performs the parameter estimation in a dimension-reduced parameter subspace by exploiting the a priori analytical structure of the 2-D phase error, therefore possesses much higher accuracy and efficiency than conventional blind methods. Finally, experimental results clearly demonstrate the effectiveness and robustness of the proposed method.

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Two‐dimensional autofocus technique for high‐resolution spotlight synthetic aperture radar

Cited by 19 publications

References 20 publications

Squinted Airborne Synthetic Aperture Radar Imaging with Unknown Curved Trajectory

Squinted Airborne Synthetic Aperture Radar Imaging with Unknown Curved Trajectory

A Modified Iteration-Free SPGA Based on Removing the Linear Phase

Knowledge-Aided 2-D Autofocus for Spotlight SAR Filtered Backprojection Imagery

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