## 5 Experimental Results
### Simulation Experiment
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**True**} & \multicolumn{2}{c}{**Proposed Method**} & \multicolumn{2}{c}{**KT-CICPF**} \\ \cline{3-6}  & & **Estimate** & **Ero\%** & **Estimate** & **Ero\%** \\ \hline \(a_{1}\) & \(-3.00000000\) & \(-2.99384938\) & 0.20502050 & \(-3.01689870\) & 0.56329000 \\ \(a_{2}\) & 1.42160000 & 1.42090117 & 0.04915745 & 1.32186099 & 7.01596862 \\ \(a_{3}\) & \(-0.01864704\) & \(-0.01868167\) & 0.18571902 & \(-0.01828961\) & 1.916818969 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The motion parameter estimation results of the point target obtained by the two methods. Figure 5: The focused images of point target. (**a**) Range compression. (**b**) The proposed DCCF. (**c**) DCCF constructed by KT-CICPF. (**d**) Peak signal extracted by DCCF. (**e**) Spectrum of peak signal. (**f**) \(F_{0}\)–\(F_{1}\) parametric plane by proposed method. (**g**) \(F_{0}\)–\(F_{1}\) 1 parametric plane by KT-CICPF. (**h**) \(F_{0}\) slice. (**i**) Image of the proposed method (64\(\times\) upsampling). (**j**) Image of the KT-CICPF method (64\(\times\) upsampling). (**k**) Range slice (64\(\times\) upsampling). (**l**) Azimuth slice (64\(\times\) upsampling). the Doppler bandwidth of the moving target in the synthetic aperture time can be calculated. So, the theoretical azimuth resolution is \(0.886(v/B_{a})=0.1256\) m. As can be seen from the table, the resolution obtained by the proposed method is closer to the theoretical value than that obtained by KT-CICPF method. The azimuth resolution of the KT-CICPF method has a maximum broadening of about 26.51%. The theoretical values of the peak sidelobe ratio (PSLR) and integrated sidelobe ratio (ISLR) are \(-13.27\) and \(-10.24\) dB [10]. For the proposed method, the range and azimuth PSLR/ISLR values have small deviations from the theoretical values. Figure 5i,j show the imaging results of the two methods, respectively. Figure 5k,l are slices of the imaging results in the range direction and azimuth direction. It can be seen that the proposed method has a better focusing performance. * **Distributed Target** In high-resolution SAR images, the target is often composed of multiple pixels. In order to verify the ability of the algorithm to maintain the target geometry, this paper carried out the simulation analysis of moving targets with \"cross\" distribution (hereinafter referred to as T1) and \"circular\" distribution (hereinafter referred to as T2). T1 consists of 15 strong scattering points. The azimuth and range intervals between the scattering points are both 0.5 m, and the coordinate distribution is shown in Figure 6a, where the red squares represent the positions of the scattering points. T2 is composed of 63 scattering points uniformly distributed on the circumference, and its coordinate distribution is shown in Figure 6b. In Figure 6, the abscissa is the slant range, and the ordinate is the azimuth coordinate. Two methods are used to process it. Figures 7 and 8 show the processing results of T1 and T2, respectively. Figures 7a,d and 8a,d are the amplitude and spectrum of the chirp signal corresponding to the DCCF peak range cell, which are used to estimate the coefficient of the third-order term. The signal gain and resolution extracted in the proposed method are higher than those extracted by KT-CICPF. Figure 8b,e are the \(F_{0}\)-\(F_{1}\) parameter planes constructed by the two methods for the target T2, respectively. From the figures, it can be seen that the peak in the \(F_{0}\)-\(F_{1}\) plane constructed by the proposed method is