Minimum-Entropy-Based Autofocus Algorithm for SAR Data Using Chebyshev Approximation and Method of Series Reversion, and Its Implementation in a Data Processor

Xiong, Tao; Xing, Mengdao; Wang, Yong; Wang, Shuang; Sheng, Jialian; Guo, Liang

doi:10.1109/tgrs.2013.2253781

Cited by 55 publications

(23 citation statements)

References 16 publications

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“…• The s dependency of the position errors in (16) and (20) explains the smearing observed in the SAR images. Thus, the space-varying positioning errors and smearing due to the antenna trajectory errors observed in the reconstructed images can be analyzed quantitatively using (16) and (20).…”

Section: Analysis Of Positioning Errors In Bistatic Sar Images Due Tomentioning

confidence: 98%

“…• The positioning error △z for a target located at z is given by the solution of (16) and (20). • (16) gives the target positioning error along the bistatic look-direction and (20) describes the positioning error in the transverse bistatic look-direction. • From (16) and 20, we see that the radial position error △ Ξ z mainly depends on the radial antenna trajectory errors,…”

Section: Analysis Of Positioning Errors In Bistatic Sar Images Due Tomentioning

confidence: 99%

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Antenna motion errors in bistatic SAR imagery

Yazıcı

Yanık

2015

Inverse Problems

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Antenna trajectory or motion errors are pervasive in synthetic aperture radar (SAR) imaging. Motion errors typically result in smearing and positioning errors in SAR images. Understanding the relationship between the trajectory errors and position errors in reconstructed images is essential in forming focused SAR images. Existing studies on the effect of antenna motion errors are limited to certain geometries, trajectory error models or monostatic SAR configuration. In this paper, we present an analysis of position errors in bistatic SAR imagery due to antenna motion errors. Bistatic SAR imagery is becoming increasingly important in the context of passive imaging and multi-sensor imaging. Our analysis provides an explicit quantitative relationship between the trajectory errors and the positioning errors in bistatic SAR images. The analysis is applicable to arbitrary trajectory errors and arbitrary imaging geometries including wide apertures and large scenes. We present extensive numerical simulations to validate the analysis and to illustrate the results in commonly used bistatic configurations and certain trajectory error models.

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Section: Analysis Of Positioning Errors In Bistatic Sar Images Due Tomentioning

confidence: 98%

Section: Analysis Of Positioning Errors In Bistatic Sar Images Due Tomentioning

confidence: 99%

Antenna motion errors in bistatic SAR imagery

Yazıcı

Yanık

2015

Inverse Problems

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“…(e) Output. The convergent linear coefficient k is output, and then the azimuth-variant phase error is compensated easily by the interpolation process in Equations (24) and (25).…”

Section: Procedures Of Proposalmentioning

confidence: 99%

“…In practical applications, previous widely used autofocus algorithms include classical phase gradient autofocus (PGA) [17], map-drift algorithm (MDA) [18,19] and image metric-based autofocus algorithms, such as contrast or entropy optimization [20][21][22][23][24]. The PGA retrieves high-order terms of phase error by exploiting the phase gradient of prominent scatters, so its performance is dependent on the number of prominent scatters and is generally affected by noise and clutter.…”

Section: Introductionmentioning

confidence: 99%

“…Metric-based autofocus methods usually produce superior restorations compared with the conventional ones [20]. An optimization strategy is designed through maximizing (minimizing) a particular sharpness (entropy or sparsity) metric to estimate phase error parameters [21][22][23][24]. All these algorithms above are designed for compensating spatial-invariant phase errors in SAR imagery based on the hypothesis of narrow beam [25], which neglects the spatial-variant components.…”

Section: Introductionmentioning

confidence: 99%

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Robust Two-Dimensional Spatial-Variant Map-Drift Algorithm for UAV SAR Autofocusing

et al. 2019

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Autofocus has attracted wide attention for unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) systems, because autofocus process is crucial and difficult when the phase error is spatially dependent on both range and azimuth directions. In this paper, a novel two-dimensional spatial-variant map-drift algorithm (2D-SVMDA) is developed to provide robust autofocusing performance for UAV SAR imagery. This proposed algorithm combines two enhanced map-drift kernels. On the one hand, based on the azimuth-dependent phase correction, a novel azimuth-variant map-drift algorithm (AVMDA) is established to model the residual phase error as a linear function in the azimuth direction. Then the model coefficients are efficiently estimated by a quadratic Newton optimization with modified maximum cross-correlation. On the other hand, by concatenating the existing range-dependent map-drift algorithm (RDMDA) and the proposed AVMDA in this paper, a phase autofocus procedure of 2D-SVMDA is finally established. The proposed 2D-SVMDA can handle spatial-variance problems induced by strong phase errors. Simulated and real measured data are employed to demonstrate that the proposed algorithm compensates both the range- and azimuth-variant phase errors effectively.

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Calibrating for the satellite attitude jitter in multistatic SAR using a subspace method

Liu

Wang

et al. 2017

IET Radar, Sonar & Navigation

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Minimum-Entropy-Based Autofocus Algorithm for SAR Data Using Chebyshev Approximation and Method of Series Reversion, and Its Implementation in a Data Processor

Cited by 55 publications

References 16 publications

Antenna motion errors in bistatic SAR imagery

Antenna motion errors in bistatic SAR imagery

Robust Two-Dimensional Spatial-Variant Map-Drift Algorithm for UAV SAR Autofocusing

Calibrating for the satellite attitude jitter in multistatic SAR using a subspace method

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