Entropy-based motion error correction for high-resolution spotlight SAR imagery

Yang, Lichao; Xing, Mengdao; Zhang, L.; Sheng, Jialian; Bao, Zheng

doi:10.1049/iet-rsn.2011.0078

Cited by 44 publications

(24 citation statements)

References 11 publications

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“…In terms of the estimated phase type, there are three categories for the APE estimation, the phase rate estimation, the phase difference estimation and the direct phase estimation. Map-Drift (MD) [25], Phase Gradient Autofocusing (PGA) [21] and Minimum Entropy Autofocusing (MEA) [26] are the typical methods, respectively. Considering both the accuracy and efficiency of the APE error estimation, PGA is the best candidate, which has been widely used in the SAR community.…”

Section: Coherent Motion Error Compensationmentioning

confidence: 99%

Coherent Auto-Calibration of APE and NsRCM under Fast Back-Projection Image Formation for Airborne SAR Imaging in Highly-Squint Angle

et al. 2018

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Synthetic Aperture Radar (SAR) imaging with a non-zero (forward) squint angle is capable of providing a longer time for reaction than that of the broadside mode. However, due to the large squint angle, there will be severe coupling between range and azimuth samples in the echoed data, which is known as the problematic Range Cell Migration (RCM) in the SAR community. Especially when the SAR sensor mounted on an airborne platform encounters unexpected motion deviations/errors, the coupling becomes more complicated, and it is difficult to differentiate the systematic RCM for the SAR Image Formation Processing (IFP) and the non-systematic RCM error to be compensated. To this end, a novel and accurate SAR imaging algorithm is proposed in this paper to facilitate the processing of airborne SAR data collected at a high-squint angle. Firstly, the proposed algorithm is established under a Fast Time-Domain Back-Projection (FTDBP) framework for the SAR IFP. FTDBP paves the way to avoid the complicated processing for the systematic RCM as for the conventional SAR IFP in the Doppler processing manner. It is capable of generating a high-resolution SAR image efficiently under more general geometries and configurations. Secondly, regarding the non-systematic RCM errors, the proposed algorithm realizes the compensation by correcting both the Non-systematic Range Cell Migration (NsRCM), as well as Azimuthal Phase Error (APE) in a coherent manner. It is consequently capable of auto-calibrating the effects of the motion error completely without being dependent on the airborne navigation unit. Finally, both simulated and raw data collected by the airborne squinted SAR are applied to evaluate the proposed algorithm. Comparisons with conventional algorithms are carried out to reveal the superiority of the proposed algorithm.

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Section: Coherent Motion Error Compensationmentioning

confidence: 99%

Coherent Auto-Calibration of APE and NsRCM under Fast Back-Projection Image Formation for Airborne SAR Imaging in Highly-Squint Angle

et al. 2018

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“…In addition to those visual result, we further use image entropy and the correlation value [40] with the reference image as two criteria to quantitatively evaluate the quality of the recovered images after random sampling. In the correlation criterion, the gray level image obtained by the RDA using the original dataset with full measurements is used as the reference image denoted by X o .…”

Section: E Practical Data Experiments With Data Set "Yak-42"mentioning

confidence: 99%

Enhanced ISAR Imaging by Exploiting the Continuity of the Target Scene

Wang

Zhao

et al. 2014

IEEE Trans. Geosci. Remote Sensing

119

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“…The conventional autofocus techniques, such as mapdrift autofocus [9], Phase Gradient Autofocus (PGA) [10] and Minimum-Entropy Method (MEM) [11], which based on the observation mathematical model in ISAR are accurate and belong to the post-processing approaches. Although these autofocus techniques are simple to implement and computationally efficient, they usually suffer from high sidelobe levels which visually smears the target signature and cannot be easy integrated into the CS framework to estimate the phase error and obtain the high focus target ISAR image.…”

Section: Introductionmentioning

confidence: 99%

Joint ISAR Imaging and Phase Error Correction Based on Sparse Bayesian Learning

Su¹,

Qin

Wang

et al. 2016

IJSPS

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The ISAR imaging algorithm has depend on the mathematical model of the observation process, and the inaccuracies in the observation model may cause the model errors. In this paper, ISAR imaging is regarded as a narrow-band version of the Computer Aided Tomography (CT), where the phase errors in ISAR data are treated as model errors. Based on the inherent sparsity of targets in the imaging area, the ISAR imaging joint with phase adjustment is represented by a sparse signal reconstruction problem, which is set up as an optimization problem in a Sparse Bayesian Learning (SBL) framework. Owing to the superiority of the SBL, we employ an expansioncompression variance-component based method (ExCoV) to reconstruct the target's scattering coefficients and correct the phase error alternately via the maximum likelihood estimation. The numerical simulation results show the effectiveness of this novel method for various types of phase errors, which can produce a relatively well-focused image of the target and obtains the improvements over existing techniques for model error compensation in ISAR. 

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Entropy-based motion error correction for high-resolution spotlight SAR imagery

Cited by 44 publications

References 11 publications

Coherent Auto-Calibration of APE and NsRCM under Fast Back-Projection Image Formation for Airborne SAR Imaging in Highly-Squint Angle

Coherent Auto-Calibration of APE and NsRCM under Fast Back-Projection Image Formation for Airborne SAR Imaging in Highly-Squint Angle

Enhanced ISAR Imaging by Exploiting the Continuity of the Target Scene

Joint ISAR Imaging and Phase Error Correction Based on Sparse Bayesian Learning

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