## 3 Results
### Experiments with Anechoic Chamber Measurement Data  
Measurement data were collected from the echo data of space targets in a microwave anechoic chamber. The dimensions of the solid model were the same as the dimensions of the 3D model shown in Figure 8. The sweep frequency ranged from 8.75 GHz to 10.75 GHz, while the step size was 0.02 GHz. Similarly, to provide a clearer display of the interpretation results, ISAR imaging was performed on the anechoic chamber measurement  
Figure 11: Interpretation results of the proposed method at imaging angles of 30\({}^{\circ}\) (**a**), 45\({}^{\circ}\) (**b**), and 90\({}^{\circ}\) (**c**) when the simulation data were labeled with the components names. Figure 12: Interpretation results of the method based on Cloude–Pottier decomposition at imaging angles of 30\({}^{\circ}\) (**a**), 45\({}^{\circ}\) (**b**), and 90\({}^{\circ}\) (**c**) when the data were labeled with the component names. Figure 13: Interpretation results of the method based on Krogager decomposition at imaging angles of 30\({}^{\circ}\) (**a**), 45\({}^{\circ}\) (**b**), and 90\({}^{\circ}\) (**c**) when the data were labeled with the component names. data. Figure 14 displays the Pauli images at imaging angles of 45\({}^{\circ}\), 90\({}^{\circ}\), and 120\({}^{\circ}\). The interpretation results obtained from different methods are also marked on the ISAR images. The structure interpretation method based on Cameron decomposition faces the same problem encountered during the previous simulation experiments. The results in Figure 15 show a considerable degree of confusion, particularly in the location of the target's tail. This interpretation result deviates significantly from the actual structure of the target. The classifier identifies scattering centers as different types of canonical structures, such as \"Trihedral/Plate\", \"Dipole\", and \"Quarter-Wave\" (as illustrated in Figure 15a-c). There is no practical information that would allow us to recognize the target from Figure 15. In contrast, the interpretation results of the method presented in this paper align more closely with the actual structure of the target when the labels are composed of canonical structures. Figure 16 shows the results of the proposed method when the data are labeled with the component names. Except for some scattering centers on the target's fairing, the proposed method can interpret the data relatively accurately. Krogager decomposition and Cloude-Pottier decomposition were also applied to the measured data. Compared with single-polarization decomposition methods, the proposed method integrates multiple polarization scattering mechanisms. Therefore, the proposed method is theoretically more capable of characterizing components. Figures 16-18 testify to this point. According to Figure 17, Cloude-Pottier decomposition still incorrectly identifies all scattering centers of the target as fairings, which shows that the Cloude-Pottier decomposition coefficients are less discriminative for the target components. Although the overall accuracy results of the two methods are 91.6% and 82.0%, respectively, comparing Figures 16 and 18 seems to indicate that there is no significant performance advantage between the Krogager method and the proposed method. However, the actual performance gap may be narrower due to material property mismatch. There are differences between the Pauli images in Figures 9 and 14 due to the influence of surface roughness, material properties, and other factors on the physical model. Sporadic scattering centers appear in areas where there were none before, especially at an imaging angle of 90 degrees (Figure 9b). The benefit of this is that the shape of the target is better  
Figure 16: Interpretation results of the proposed method at imaging angles of 45\({}^{\circ}\) (**a**), 90\({}^{\circ}\) (**b**), and 120\({}^{\circ}\) (**c**) when the data were labeled with the component names.