Ultrahigh-Resolution Autofocusing for Squint Airborne SAR Based on Cascaded MD-PGA

Jin, Yanghao; Chen, Jianlai; Xia, Xiang-Gen; Liang, Buge; Xiong, Yi; Liang, Zhihuan; Xing, Mengdao

doi:10.1109/lgrs.2021.3105254

Cited by 10 publications

(5 citation statements)

References 22 publications

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“…The dashed trajectory represents the ideal motion trajectory of the platform, while the actual trajectory is similar to the motion state described by the solid trajectory. In order to solve the problems of Doppler frequency shift and image defocusing caused by motion errors, this study mainly adopts the MD algorithm based on data-driven motion error estimation and compensation to achieve autofocus in real-time ViSAR imaging [28,29]. The core idea of the MD algorithm can be formulated by the following steps.…”

Section: Algorithmsmentioning

confidence: 99%

Miniaturization Design of High-Integration Unmanned Aerial Vehicle-Borne Video Synthetic Aperture Radar Real-Time Imaging Processing Component

Yang,

Wang,

Zheng

et al. 2024

Remote Sensing

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The unmanned aerial vehicle (UAV)-borne video synthetic aperture radar (SAR) possesses the characteristic of having high-continuous-frame-rate imaging, which is conducive to the real-time monitoring of ground-moving targets. The real-time imaging-processing system for UAV-borne video SAR (ViSAR) requires miniaturization, low power consumption, high frame rate, and high-resolution imaging. In order to achieve high-frame-rate real-time imaging on limited payload-carrying platforms, this study proposes a miniaturization design of a high-integration UAV-borne ViSAR real-time imaging-processing component (MRIPC). The proposed design integrates functions such as broadband signal generation, high-speed real-time sampling, and real-time SAR imaging processing on a single-chip FPGA. The parallel access mechanism using multiple sets of high-speed data buffers increases the data access throughput and solves the problem of data access bandwidth. The range-Doppler (RD) algorithm and map-drift (MD) algorithm are optimized using parallel multiplexing, achieving a balance between computing speed and hardware resources. The test results have verified that our proposed component is effective for the real-time processing of 2048 × 2048 single-precision floating-point data points to realize a 5 Hz imaging frame rate and 0.15 m imaging resolution, satisfying the requirements of real-time ViSAR-imaging processing.

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Section: Algorithmsmentioning

confidence: 99%

Miniaturization Design of High-Integration Unmanned Aerial Vehicle-Borne Video Synthetic Aperture Radar Real-Time Imaging Processing Component

Yang,

Wang,

Zheng

et al. 2024

Remote Sensing

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“…However, dominant targets should be abundant (at multiple range bins across the swath) and they should be free from any other defocusing than the azimuth phase error. It also requires high SNR for the scene similar to PGA [4]. Therefore before applying PCA, the quantity and quality of the targets need to be ensured.…”

Section: B Pcamentioning

confidence: 99%

“…For stripmap SAR, nonparametric phase curvature autofocus (PCA) is commonly used [3]. As a slightly modified version of the phase gradient autofocus (PGA), PCA offers estimation of both low and high frequency motion errors, including quadratic and higher orders [4]. However, similar to PGA, PCA relies on dominant scatterers in the SAR scene for the estimation process [5].…”

Section: Introductionmentioning

confidence: 99%

“…Combination of parametric and nonparametric autofocus can be found in the existing literature. Notably, MD and PGA are cascaded by Jin et al [4] to compensate the residual range cell migration (RCM) in squint SAR. MAM, weighted PGA and contrast-optimization (CO) based autofocus are combined to compensate the residual RCM, along-track velocity and range-dependent phase error [7].…”

Section: Introductionmentioning

confidence: 99%

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Combined MAM-PCA autofocus for stripmap SAR

Afroz

Abbott

et al. 2022

2022 23rd International Radar Symposium (IRS)

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The nonparametric phase curvature algorithm (PCA) is commonly used to estimate residual motion error in stripmap SAR. The algorithm is capable of estimating second and higher order errors, as well as high frequency errors. However, its dependence on dominant scatterers restricts its application to a wide variety of scenes. Such a limitation is encountered in focusing a polarimetric L-band SAR dataset collected from an agricultural field. The lack of dominant scatterers in the scene results in inaccurate error estimation when PCA is applied directly. Another autofocus scheme, the Multi-aperture Mapdrift (MAM), is used first to eliminate the low order motion errors. This improves the focusing quality of the existing targets. The enhanced point targets are then suitable for PCA to remove the remaining higher order motion errors. The novel MAM-PCA combination significantly improves the image contrast compared to that obtained from the approaches applied separately. Point target based comparison of MAM, PCA and MAM-PCA show that the proposed technique improves the overall quality of the target profile.

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“…Synthetic aperture sonar (SAS) provides a high resolution imagery of the seafloor by the coherent processing of signals collected over space and time [1][2][3][4]. However, the imaging quality of SAS is foremost limited by the accurate estimation of platform motions, which requires sub-wavelength knowledge [5][6][7]. This has proven to be challenging in GPS-denied environments, with even high-grade navigation devices potentially not able to achieve sufficient accuracy [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Deep learning‐based time delay estimation for motion compensation in synthetic aperture sonars

Chen,

Chi,

Zhang

et al. 2023

IET Radar Sonar & Navi

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Accurate and robust time delay estimation is crucial for synthetic aperture sonar (SAS) imaging. A two‐step time delay estimation method based on displaced phase centre antenna (DPCA) micronavigation has been widely applied in motion estimation and compensation of SASs. However, the existing methods for time delay estimation are not sufficiently robust, which reduces the performance of SAS motion estimation. Deep learning is currently one of the cutting‐edge techniques and has brought about a remarkable progress in the field of underwater acoustic signal processing. In this study, a deep learning‐based time delay estimation method is introduced to SAS motion estimation and compensation. The subband processing is first applied to obtain ambiguous time delays between adjacent pings from phases of SAS echoes. Then, a lightweight neural network is utilised to construct phase unwrapping. The model of the employed neural network is trained with simulation data and applied to real SAS data. The results of time delay estimation and motion compensation demonstrate that the proposed neural network‐based method has much better performance than the two‐step and joint‐subband methods.

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Ultrahigh-Resolution Autofocusing for Squint Airborne SAR Based on Cascaded MD-PGA

Cited by 10 publications

References 22 publications

Miniaturization Design of High-Integration Unmanned Aerial Vehicle-Borne Video Synthetic Aperture Radar Real-Time Imaging Processing Component

Miniaturization Design of High-Integration Unmanned Aerial Vehicle-Borne Video Synthetic Aperture Radar Real-Time Imaging Processing Component

Combined MAM-PCA autofocus for stripmap SAR

Deep learning‐based time delay estimation for motion compensation in synthetic aperture sonars

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