Efficiency and Robustness Improvement of Airborne SAR Motion Compensation With High Resolution and Wide Swath

Chen, Jianlai; Liang, Buge; Zhang, Junchao; Yang, Dongkai; Deng, Yuhui; Xing, Mengdao

doi:10.1109/lgrs.2020.3031304

Cited by 21 publications

(15 citation statements)

References 15 publications

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“…ue to atmospheric disturbance or pilot error, the actual motion trajectory of an airborne synthetic aperture radar (SAR) may easily deviate from a straight line, and the acquired raw data cannot be directly focused due to the existence of motion errors [1][2][3][4][5][6]. Such motion errors can be compensated by measuring the deviations, through the inertial navigation system (INS) and global positioning system (GPS) unit.…”

Section: Introductionmentioning

confidence: 99%

“…For the SPE estimation, Ding et al [19] use the resampling weight PGA to estimate the SPEs in the SA Back-Projection imageries. Chen et al [3] combine PGA and the weighted least square method to improve the robustness of PGA. Mao et al [12] propose a knowledge-aided 2-D autofocus method using PGA, in which PGA is employed to estimate the azimuth-frequency phase error.…”

Section: Introductionmentioning

confidence: 99%

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A High-Resolution Airborne SAR Autofocusing Approach Based on SR-PGA and Subimage Resampling With Precise Hyperbolic Model

Deng

Wang

Sun

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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High-resolution airborne synthetic aperture radar (SAR) imaging requires long synthetic aperture time (LSAT). However, the LSAT invalidates the Fresnel approximation in the traditional autofocusing method, leaving a residual signal phase in the motion error. To solve the problem, a signal-reconstructionbased phase gradient autofocus (SR-PGA) and a sub-image resampling (SIR) methods are proposed. They are developed on the hyperbolic model. First, a sub-aperture division strategy divides the full-aperture high-order error into multiple low-order sub-aperture errors. Then, the SR-PGA is developed to estimate the sub-aperture error (SPE), in which the precise deramping is reconstructed to eliminate the residual signal phase in the SPE. Third, the SIR is proposed to eliminate residual Doppler-variant shift of the adjacent sub-images, improving the accuracy of the SPE combination. Finally, simulation and actual data processing verify the effectiveness and validity of the algorithm.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A High-Resolution Airborne SAR Autofocusing Approach Based on SR-PGA and Subimage Resampling With Precise Hyperbolic Model

Deng

Wang

Sun

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…For UAV SAR imaging, a series of autofocus methods based on phase gradient autofocus (PGA) [14] and mapdrift (MD) [15] have been proposed to estimate the residual phase error after conventional MOCO [16]. The advantage of PGA based algorithm is recognized.…”

Section: Introductionmentioning

confidence: 99%

Coherent range-dependent map-drift algorithm for improving SAR motion compensation

Zhang

Wang²,

Wei

et al. 2023

J. of Syst. Eng. Electron.

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Synthetic aperture radar (SAR) is usually sensitive to trajectory deviations that cause serious motion error in the recorded data. In this paper, a coherent range-dependent mapdrift (CRDMD) algorithm is developed to accommodate the range-variant motion errors. By utilizing the algorithm as an estimate core, robust motion compensation strategy is proposed for unmanned aerial vehicle (UAV) SAR imagery. CRDMD outperforms the conventional map-drift algorithms in both accuracy and efficiency. Real data experiments show that the proposed approach is appropriate for precise motion compensation for UAV SAR.

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“…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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Efficiency and Robustness Improvement of Airborne SAR Motion Compensation With High Resolution and Wide Swath

Cited by 21 publications

References 15 publications

A High-Resolution Airborne SAR Autofocusing Approach Based on SR-PGA and Subimage Resampling With Precise Hyperbolic Model

A High-Resolution Airborne SAR Autofocusing Approach Based on SR-PGA and Subimage Resampling With Precise Hyperbolic Model

Coherent range-dependent map-drift algorithm for improving SAR motion compensation

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

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