# A High-Resolution, Wide-Swath SAR Imaging System Based on Tandem SAR Satellites
## 4 Verification and Analysis
### Verification of Real Airborne SAR Data  
This section uses real airborne SAR data obtained in January 2017 in Crown Head Ridge, Guangxi, China, to verify the performance of the proposed imaging system; the parameters are listed in Table 6.  
The optical image of the observed scene is shown in Figure 12. The SAR imaging result processed by the traditional BP algorithm [27] is presented in Figure 13a, where a strong target is marked. Since the airborne SAR system works in strip-map mode, the original SAR data are randomly downsampled by a factor of six to construct the received echo of one subswath in the proposed system. The imaging result of the traditional BP algorithm with downsampled SAR data is presented in Figure 13b, which is seriously polluted by the ambiguity energy.  
The result of the proposed algorithm, \(\hat{\mathbf{\sigma}}_{\text{sum}}\), is shown in Figure 14c, and the coarse observation of Sat-1 and the result of the L1 algorithm are shown in Figure 14a,b for comparison.  
According to Figure 14, the coarse observation in Figure 14a has low azimuth resolution, making the targets stack together and reducing imaging interpretation performance. The result of the L1 algorithm in Figure 14b improves the imaging resolution, but some weak target information is lost, and there are a lot of reconstruction errors in the imaging result. The proposed algorithm provides the best imaging performance, as shown in Figure 14c. It not only achieves a resolution similar to the original image as shown in Figure 13a, but also suppresses the targets' side lobes. Then, the azimuth resolution, PSRR (peak side lobe ratio), ISLR (integral side lobe ratio), and SSIM (  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline Carrier frequency (GHz) & 35 \\ Range bandwidth (MHz) & 480 \\ Sampling frequency (MHz) & 500 \\ Pulse width (\(\upmu\)s) & 25 \\ Slant range (km) & 27.21 \\ Velocity (m/s) & 90.86 \\ Doppler bandwidth (MHz) & 600 \\ Duration time (s) & 15 \\ \hline \end{tabular}
\end{table}
Table 6: System parameters for airborne SAR.  
Figure 12: Panorama of the observed scene.  
measure) are used to quantitatively evaluate the imaging quality. The calculation method of SSIM is represented in Equation (23), and all of the evaluation results are listed in Table 7.  
\[\text{SSIM}\big{(}\text{Im}_{x},\text{Im}_{y}\big{)}=\frac{\big{(}2\mu_{x}\mu_{y}+ c_{1}\big{)}\big{(}2s_{xy}+c_{2}\big{)}}{\Big{(}\mu_{x}^{2}+\mu_{y}^{2}+c_{1} \big{)}\Big{(}s_{x}^{2}+s_{y}^{2}+c_{2}\Big{)}} \tag{23}\]  
where \(\text{Im}_{x}\) and \(\text{Im}_{y}\) are the evaluated image and the reference image, respectively. \(\mu_{x}\) and \(\mu_{y}\) denote the average value of \(\text{Im}_{x}\) and \(\text{Im}_{y}\), respectively. \(s_{y}^{2}\) and \(s_{x}^{2}\) denote the variance of \(\text{Im}_{x}\) and \(\text{Im}_{y}\), respectively. \(s_{xy}\) is the covariance of \(\text{Im}_{x}\) and \(\text{Im}_{y}\).  
Figure 14: _Cont._  
Figure 13: SAR images of traditional BP algorithm. (**a**) Result of original SAR data. (**b**) Result of downsampled SAR data.  
The evaluation results in Table 6 are consistent with the imaging results in Figure 13. The L1 algorithm achieves high azimuth resolution, but the reconstruction errors lead to the deterioration of SSIM. The proposed algorithm achieves high azimuth resolution and higher sidelube suppression performance. Compared with the reference result, the proposed algorithm improves the PSLR and ISLR by 5.58 dB and 9.76 dB, respectively.