A Deconvolution-Based Grating Lobes Reduction for Low-Oversampled Staggered SAR Image.
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| Title: | A Deconvolution-Based Grating Lobes Reduction for Low-Oversampled Staggered SAR Image. |
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| Authors: | Chen, Wenjiao1 (AUTHOR), Geng, Jiwen2 (AUTHOR) gengjiwen@buaa.edu.cn, Yu, Jindong3 (AUTHOR), Wang, Chenguang1 (AUTHOR), Yuan, Limin1,2 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1489. 18p. |
| Subjects: | Synthetic aperture radar, Deconvolution (Mathematics), Impulse response, Signal processing, Spatial resolution |
| Abstract: | Highlights: What are the main findings? The position-invariant property of azimuth grating lobes in low-oversampled staggered synthetic aperture radar (LS-SAR) images is theoretically verified, and the LS-SAR image on the same range cell is modeled as the convolution of the scattering scene with the system point spread function (PSF) plus additive noise. A deconvolution-based grating lobes reduction method combining numerically calculated PSF and Lucy–Richardson (LR) iterative deconvolution is proposed, which effectively reduces azimuth grating lobes and improves azimuth resolution of LS-SAR images without the restriction on the observed scene. What are the implications of the main findings? The proposed method breaks the limitations of traditional methods, which echo reconstruction and compressed sensing-based methods have the restriction on the observed scene and the complex computation, providing a new technical approach for LS-SAR image quality improvement. The method is validated by simulated point-array targets, real SAR images and airborne measured LS-SAR data, and it can solve the grating lobe and defocusing problems in actual LS-SAR data processing, providing a technical foundation for the engineering application of high-resolution wide-swath LS-SAR systems. The nonuniform raw data due to the varying pulse repetition interval (PRI) and the loss of echo pulses inevitably introduce azimuth grating lobes in the low-oversampled staggered synthetic aperture radar (LS-SAR) images, which result in ghost artifacts. In this paper, a deconvolution-based grating lobes reduction method for LS-SAR images is proposed to improve image quality. Firstly, the position-invariant property of azimuth grating lobes is theoretically analyzed and verified, and the LS-SAR image on the same range cell is mathematically modeled as the convolution between the scattering scene and the point spread function (PSF) of the LS-SAR imaging system, accompanied by the additive noise. Then, the PSF is numerically calculated according to the LS-SAR sampling strategy, the measured azimuthal antenna pattern, and the BP (Back Projection) imaging method. Finally, based on the Lucy–Richardson (LR) iterative deconvolution principle, the recovery of observed scenes and grating lobes reduction can be simultaneously achieved by deconvoluting the LS-SAR image with the acquired PSF. Both simulated experiments with point-array targets and real SAR images, as well as validation experiments with airborne measured LS-SAR data, demonstrated the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? The position-invariant property of azimuth grating lobes in low-oversampled staggered synthetic aperture radar (LS-SAR) images is theoretically verified, and the LS-SAR image on the same range cell is modeled as the convolution of the scattering scene with the system point spread function (PSF) plus additive noise. A deconvolution-based grating lobes reduction method combining numerically calculated PSF and Lucy–Richardson (LR) iterative deconvolution is proposed, which effectively reduces azimuth grating lobes and improves azimuth resolution of LS-SAR images without the restriction on the observed scene. What are the implications of the main findings? The proposed method breaks the limitations of traditional methods, which echo reconstruction and compressed sensing-based methods have the restriction on the observed scene and the complex computation, providing a new technical approach for LS-SAR image quality improvement. The method is validated by simulated point-array targets, real SAR images and airborne measured LS-SAR data, and it can solve the grating lobe and defocusing problems in actual LS-SAR data processing, providing a technical foundation for the engineering application of high-resolution wide-swath LS-SAR systems. The nonuniform raw data due to the varying pulse repetition interval (PRI) and the loss of echo pulses inevitably introduce azimuth grating lobes in the low-oversampled staggered synthetic aperture radar (LS-SAR) images, which result in ghost artifacts. In this paper, a deconvolution-based grating lobes reduction method for LS-SAR images is proposed to improve image quality. Firstly, the position-invariant property of azimuth grating lobes is theoretically analyzed and verified, and the LS-SAR image on the same range cell is mathematically modeled as the convolution between the scattering scene and the point spread function (PSF) of the LS-SAR imaging system, accompanied by the additive noise. Then, the PSF is numerically calculated according to the LS-SAR sampling strategy, the measured azimuthal antenna pattern, and the BP (Back Projection) imaging method. Finally, based on the Lucy–Richardson (LR) iterative deconvolution principle, the recovery of observed scenes and grating lobes reduction can be simultaneously achieved by deconvoluting the LS-SAR image with the acquired PSF. Both simulated experiments with point-array targets and real SAR images, as well as validation experiments with airborne measured LS-SAR data, demonstrated the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101489 |
