## 4 Numerical and Anechoic Chamber Experiments
### Comparisons with other algorithms
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Networks** & **P** & **R** & **F1** & **mAP50** & **mAP50-95** \\ \hline YOLOv3 & 0.484 & 0.054 & 0.097 & 0.054 & 0.010 \\ YOLOv5 & **0.991** & 0.333 & 0.498 & 0.628 & 0.217 \\ YOLOv6 & 0.924 & 0.222 & 0.358 & 0.336 & 0.153 \\ YOLOv8 & 0.756 & 0.278 & 0.407 & 0.352 & 0.123 \\ YOLOv11 & 0.799 & 0.333 & 0.470 & 0.442 & 0.148 \\ Ours & 0.793 & **0.778** & **0.785** & **0.838** & **0.283** \\ \hline \end{tabular}
\end{table} 
Table 3: The performance metrics across various networks in the context of specular point analysis. Figure 13: mAP50 and mAP50-95 of different networks. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Networks** & **P** & **R** & **F1** & **mAP50** & **mAP50-95** \\ \hline YOLOv3 & 0.67 & 0.905 & 0.770 & 0.913 & 0.566 \\ YOLOv5 & 0.736 & **0.952** & 0.830 & 0.913 & 0.627 \\ YOLOv6 & 0.653 & 0.895 & 0.755 & 0.891 & 0.581 \\ YOLOv8 & 0.694 & **0.952** & 0.803 & 0.933 & 0.62 \\ YOLOv11 & 0.624 & 0.857 & 0.722 & 0.852 & 0.536 \\ Ours & **0.902** & 0.948 & **0.924** & **0.989** & **0.685** \\ \hline \end{tabular}
\end{table} 
Table 5: The performance metrics across various networks in the context of ellipse arc analysis. To illustrate the improvement effects of the combined enhancement module, namely, Dyhead + WIoU (DW), introduced in this paper on the YOLOv8 model, and to further compare the impact of different versions of WIoU, we conducted ablation experiments on the Improved-YOLOv8 model. Considering the conservation of computational resources, we only chose to add one Dyhead module. Additionally, there exist three variations of WIoU: v1, v2, and v3, among which WIoUv3 requires the determination of hyperparameters \(\gamma\) and \(\delta\)[32] with the classic values \(\gamma=1.9\) and \(\delta=3\). We show the results of mAP as an index to assess the performance of the proposed network quantitatively. The findings are presented in Table 6.  
Table 6 visually illustrates the changes in model accuracy after adding Dyhead and different versions of WIoU. According to the aforementioned results, we could sum up that when the two modules are combined, i.e., Dyhead + WIoUv1, there is a significant improvement in model performance. As previously described, the Dyhead module helps the network to adaptively detect various scales of parabolic shapes from ISAR images, while WIoU to some degree mitigates the problem of sample unevenness between simple and complex examples. D. Recognition Performance in Anechoic Chamber Measurement Data  
Figure 15 illustrates the configuration of an anechoic chamber and presents a photograph of the satellite prototype. The radar transmits a linear frequency-modulated signal. Its start frequency and end frequency are 324 GHz and 344 GHz, respectively. This is a terahertz radar system with one transmitting antenna and four receiving antennas. For this experiment, only one of the receiving channels was used. In addition, a satellite model with a length of approximately 70 cm was mounted on an accurately calibrated turntable for capturing images through various observation apertures. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **MAP(\%)** \\ \hline YOLOv8 & 0.743 \\ YOLOv8 + WIoUv3 & 0.808 \\ YOLOv8 + WIoUv2 & 0.737 \\ YOLOv8 + WIoUv1 & 0.917 \\ YOLOv8 + Dyhead & 0.906 \\ YOLOv8 + Dyhead + WIoUv3 & 0.859 \\ YOLOv8 + Dyhead + WIoUv2 & 0.926 \\
**YOLOv8 + Dyhead + WIoUv1** & **0.935** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ablation experiment. Figure 14: PR curves for three different objects. Figure 16 shows the recognition outcomes of different algorithms for the four anechoic chambers' measurement data.