Single Range Data-Based Clutter Suppression Method for Multichannel SAR

Chen, Zhanye; Zhou, Shuwei; Wang, Xing; Huang, Yan; Wan, Jun; Liu, Dong; Tan, Xiaoheng

doi:10.1109/lgrs.2021.3072411

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…A For clutter suppression, the multi-dimensional strong coupling and non-stationary clutter of CF-SAR make it impossible to obtain enough homogenous samples to construct an accurate space-time covariance matrix (STCM) for clutter suppression. For this reason, few-sample or even single-sample methods [124] have become very suitable. In addition, the high-order space-time steering vector (STSV) of clutter and unknown moving targets increases the estimation difficulty and computational complexity of the STCM and makes it more difficult to accurately solve the weights of the space-time filter.…”

Section: Interference Suppressionmentioning

confidence: 99%

Curvilinear Flight Synthetic Aperture Radar (CF-SAR): Principles, Methods, Applications, Challenges and Trends

et al. 2022

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The research into curvilinear flight synthetic aperture radar (CF-SAR) is the inevitable result of the comprehensive practicality of SAR. The flight path of the SAR platform in real applications, which is highly nonlinear or curvy due to three-dimensional velocity and acceleration, cannot be described by the traditional uniform linear motion model. New mathematical models, signal characteristics, imaging algorithms, and system design criteria must be proposed and investigated for CF-SAR. This paper provides a comprehensive overview of CF-SAR. Firstly, the basic concept, unified model, and general signal characteristics of CF-SAR are defined, derived, and analyzed, respectively. Additionally, the advantages and drawbacks of current methodologies are reviewed. Discussions on the CF-SAR’s applications are presented from the perspective of typical platforms, new configurations, and advanced technologies, which are suitable means to fulfill the increasing user requirements. Finally, the challenges faced by CF-SAR are summarized, and some future trends for the study of CF-SAR are explored. Hopefully, this paper will serve as a reference for SAR researchers/engineers and stimulate the future development and actual application of CF-SAR.

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Section: Interference Suppressionmentioning

confidence: 99%

Curvilinear Flight Synthetic Aperture Radar (CF-SAR): Principles, Methods, Applications, Challenges and Trends

et al. 2022

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“…In this case, the residual correction error ∆R LRCM after performing the compensation function in Equation ( 10) should be eliminated further. As described in Equations ( 9)- (11), the residual correction error ∆R LRCM induced by the t n -term coefficient ϕη is a form of typical LRCM. Some typical approaches, for example, the radon transform [17], Hough transform [16,37] and radon Fourier transform [38] have been suggested to remove the LRCM.…”

Section: Analysis Related To a Mismatch Of The Lrm Compensation Functionmentioning

confidence: 99%

“…However, moving targets inevitably emerge into the scene of observation. Therefore, ground moving-target imaging (GMTIm) is a research hotspot that has become important in SAR applications, due to the growing demand for moving-target surveillance [7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Fast Approach for SAR Imaging of Ground-Based Moving Targets Based on Range Azimuth Joint Processing

et al. 2022

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The synthetic aperture radar (SAR) images of a moving target may be out of focus, given the motions of a non-cooperative target. Doppler ambiguities, including the Doppler center blur and spectrum ambiguity, will easily appear due to the limitations of pulse repetition frequency, which causes difficulty in moving-target imaging. Therefore, a robust fast Doppler ambiguity approach for SAR imaging of a ground-based moving target using range azimuth joint processing (RAJP) is presented. Firstly, the use of RAJP, based on a two-dimensional cross-correlation function and linear range cell migration (LRCM) compensation function, is proposed to simultaneously obtain the first- and second-order phase parameters in the fast-time and azimuth-frequency domains. Then, a corresponding azimuth reference function is constructed to image the moving target. Additionally, a principal component analysis-based operation is introduced to solve the mismatch with the LRCM compensation function. The couplings between the range and azimuth and between the first- and second-order parameters can be simultaneously decoupled by the proposed RAJP operation, which simplifies the processing steps. The developed approach can simultaneously obtain the first- and second-order parameters in the fast-time and azimuth-frequency domains, which avoids the propagation error of parameter estimation caused by the stepwise processing operation. The proposed method is relatively fast, given the need for fewer processing steps. The presented approach is robust in terms of Doppler ambiguity and handles the blind speed sidelobe well. In this study, simulated and real data are processed to verify the proposed approach.

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“…RFI presents as striped or blocky electromagnetic artifacts in SAR images, thereby degrading image quality [9]. With high-power RFI, these artifacts can significantly obscure the entire images and it is detrimental to the observation [10]. Therefore, to fully extract the geographic information from the images, a lot of effort is required to investigate the RFI suppression approaches [11].…”

Section: Introductionmentioning

confidence: 99%

MCSNet: A Radio Frequency Interference Suppression Network for Spaceborne SAR Images via Multi-Dimensional Feature Transform

Ran

Zhang

et al. 2022

Remote Sensing

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Spaceborne synthetic aperture radar (SAR) is a promising remote sensing technique, as it can produce high-resolution imagery over a wide area of surveillance with all-weather and all-day capabilities. However, the spaceborne SAR sensor may suffer from severe radio frequency interference (RFI) from some similar frequency band signals, resulting in image quality degradation, blind spot, and target loss. To remove these RFI features presented on spaceborne SAR images, we propose a multi-dimensional calibration and suppression network (MCSNet) to exploit the features learning of spaceborne SAR images and RFI. In the scheme, a joint model consisting of the spaceborne SAR image and RFI is established based on the relationship between SAR echo and the scattering matrix. Then, to suppress the RFI presented in images, the main structure of MCSNet is constructed by a multi-dimensional and multi-channel strategy, wherein the feature calibration module (FCM) is designed for global depth feature extraction. In addition, MCSNet performs planned mapping on the feature maps repeatedly under the supervision of the SAR interference image, compensating for the discrepancies caused during the RFI suppression. Finally, a detailed restoration module based on the residual network is conceived to maintain the scattering characteristics of the underlying scene in interfered SAR images. The simulation data and Sentinel-1 data experiments, including different landscapes and different forms of RFI, validate the effectiveness of the proposed method. Both the results demonstrate that MCSNet outperforms the state-of-the-art methods and can greatly suppress the RFI in spaceborne SAR.

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Single Range Data-Based Clutter Suppression Method for Multichannel SAR

Cited by 4 publications

References 14 publications

Curvilinear Flight Synthetic Aperture Radar (CF-SAR): Principles, Methods, Applications, Challenges and Trends

Curvilinear Flight Synthetic Aperture Radar (CF-SAR): Principles, Methods, Applications, Challenges and Trends

Fast Approach for SAR Imaging of Ground-Based Moving Targets Based on Range Azimuth Joint Processing

MCSNet: A Radio Frequency Interference Suppression Network for Spaceborne SAR Images via Multi-Dimensional Feature Transform

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