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Diagram of a gridded synthetic aperture radar (SAR) observation model. The blue box represents the observed area. The red points represent discrete scattering points with large coefficients. X and Y are the number of grids in range and azimuth directions, respectively.
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Sparsity-based synthetic aperture radar (SAR) imaging has attracted much attention since it has potential advantages in improving the image quality and reducing the sampling rate. However, it is vulnerable to deliberate blanket disturbance, especially wideband noise interference (WBNI), which severely damages the imaging quality. This paper mainly...
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... t is the fast time in the range direction; τ is the slow time in the azimuth direction; τ c is the zero-Doppler time; L is the number of scattering points in observed scene; σ l is the backscatter coefficient of the l-th point; c f is the carrier frequency; c is the speed of light; l R is the oblique distance between scattering point and SAR platform; r K is the frequency modulation slope; and ⋅ ( ) In sparsity-based SAR imaging models, the observed scene is assumed to be uniformly divided into grids and composed of discrete scattering points, as shown in Figure 1. If the number of points with large scattering coefficients is much smaller than that of grids, the scene can be considered sparse in the space domain. ...
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... further investigate the WBNI suppression effects with the proposed DDSE algorithm, we extended the simulation to the case of range-azimuth reconstruction, where an aircraft target with multiple scattering points was modeled and utilized for SAR imaging. The main parameters in this part are listed in Table 5 [33,34], and the intuitive results of range-azimuth imaging based on different WBNI suppression algorithms are shown in Figure 10. In Figure 10a,b, the reconstructed aircraft target is almost covered by WBNI and can hardly be distinguished if no measures are taken. ...
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... main parameters in this part are listed in Table 5 [33,34], and the intuitive results of range-azimuth imaging based on different WBNI suppression algorithms are shown in Figure 10. In Figure 10a,b, the reconstructed aircraft target is almost covered by WBNI and can hardly be distinguished if no measures are taken. In Figure 10c,d, the BPDN algorithm has little effect on interference suppression, and the ACS algorithm leads to serious signal distortion. ...
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... Figure 10a,b, the reconstructed aircraft target is almost covered by WBNI and can hardly be distinguished if no measures are taken. In Figure 10c,d, the BPDN algorithm has little effect on interference suppression, and the ACS algorithm leads to serious signal distortion. The BSBL algorithm in Figure 10e, which is effective for narrowband interference (NBI) separation, increases the adverse effect for image reconstruction. ...
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... Figure 10c,d, the BPDN algorithm has little effect on interference suppression, and the ACS algorithm leads to serious signal distortion. The BSBL algorithm in Figure 10e, which is effective for narrowband interference (NBI) separation, increases the adverse effect for image reconstruction. In contrast, the proposed DDSE algorithm in Figure 10f performs better than the others in terms of visual quality. ...
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... BSBL algorithm in Figure 10e, which is effective for narrowband interference (NBI) separation, increases the adverse effect for image reconstruction. In contrast, the proposed DDSE algorithm in Figure 10f performs better than the others in terms of visual quality. ...
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... pi is the probability of the i-th grayscale level; and NG is the total number of all grayscale levels in the image. Figure 11 shows the statistical average of the peak-signal-to-noise ratio (PSNR) and image entropy under different ISRs from 0 dB to 30 dB by 100 numerical simulations. Figures 12 and 13 show the range-azimuth imaging results based on the DDSE algorithm under different sparsity levels and compression ratios, where the number of points in the simulated aircraft model were set to 174, 348, and 696, and the CRs were set to 1/2, 1/4, and 1/8, respectively. ...
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... 11 shows the statistical average of the peak-signal-to-noise ratio (PSNR) and image entropy under different ISRs from 0 dB to 30 dB by 100 numerical simulations. Figures 12 and 13 show the range-azimuth imaging results based on the DDSE algorithm under different sparsity levels and compression ratios, where the number of points in the simulated aircraft model were set to 174, 348, and 696, and the CRs were set to 1/2, 1/4, and 1/8, respectively. As can be seen from the results in Figure 11, the PSNR decreases with an increasing interferenceto-signal ratio (ISR), while the image entropy increases. ...
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... 12 and 13 show the range-azimuth imaging results based on the DDSE algorithm under different sparsity levels and compression ratios, where the number of points in the simulated aircraft model were set to 174, 348, and 696, and the CRs were set to 1/2, 1/4, and 1/8, respectively. As can be seen from the results in Figure 11, the PSNR decreases with an increasing interferenceto-signal ratio (ISR), while the image entropy increases. The BSBL algorithm performs better when the ISR is lower than 10 dB, but it also presents a rapid deterioration in performance. ...
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... this superiority gradually weakens as the interference power increases further. As shown in Figures 12 and 13, WBNI suppression for range-azimuth imaging is influenced by both the sparsity level and the compression ratio. Under the same conditions, the proposed DDSE algorithm performs better for the observed scene with a lower sparsity level, since the dimensions of subspace corresponding to the SOI are smaller, leading to more interference components being suppressed in the process of signal reconstruction. ...
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... In other words, directional noise can be considered a kind of main lobe broadband noise interference. Currently, the research on HFSWR narrowband interference suppression by signal processing methods is relatively in-depth, mainly including frequency domain notch methods [24], parameter estimation methods [25], characteristic subspace spatial-based filtering methods [26], adaptive beam forming methods [27], blocking matrix preprocessing methods [28], eigenprojection matrix preprocessing methods [29], and blind source separation technique [30]. Adaptive beamforming technology can automatically form a notch in the interference direction. ...
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