## 4 Discussion  
Information fusion is typically categorized into three levels--data-level, feature-level, and decision-level fusion--depending on the stage at which it occurs [29]. The proposed MFasNetV1 network performs fusion of the feature maps output by each feature extraction network, thereby achieving feature-level information fusion. Furthermore, during the testing of the target task networks in Section 3.3.3, classification probability data for each feature were obtained. By applying an average weighting method to these probabilities, a decision-level test result can be derived, yielding a test accuracy of 94.1%; while using a feature adaptation factor function to implement variable weighting values could theoretically improve the accuracy, fitting such a function becomes increasingly challenging as the number of adaptation factors grows. Given that this is not the primary focus of our current research, we will refrain from delving into it further. To facilitate more in-depth performance comparison tests, lightweight networks including ResNet18, MobileNetV2, ShuffleNetV2x1, and EfficientNetB0 were employed to construct data-level information fusion networks [30]. The dimensions of the three types of feature sample data were uniformly adjusted to 224 \(\times\) 224 and formatted into three-channel data with a [3, 224, 224] structure as the input for these networks. Initially, samples from the meta-learning library were utilized to pre-train the four networks. During the pre-training phase, the classification task involved five classes (four types of interference and underwater vehicles). Subsequently, the target task training set and validation set were used for fine-tuning and validating the networks, with the validation accuracy curve presented in Figure 19.  
In Figure 19, after three training steps, the validation accuracy of ResNet18, EfficientNetB0, and ShuffleNetV2x1 all exceeded 80%. EfficientNetB0 reached an accuracy of over 90% after nine steps, whereas MobileNetV2 and ShuffleNetV2x1 reached 90% accuracy after 14 steps. By comparing Figures 16 and 19, it is evident that the proposed MFasNetV1 network based on meta-learning demonstrates a significant advantage in terms of rapid convergence. During the training process of the target task network, for performance comparisons, both MFasNetV1 and the four lightweight networks adopted a learning rate of 0.0001. However, MFasNetV1 is capable of utilizing a higher learning rate (e.g., 0.001) to achieve faster convergence. In contrast, the four lightweight networks tend to become trapped in local optima and fail to converge when a greater learning rate (e.g., 0.0005) is \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Type** & \begin{tabular}{c} **Spatial** \\ **Feature P3 SF** \\ \end{tabular} & \
\begin{tabular}{c} **Time–Frequency** \\ **Feature ETFF** \\ \end{tabular} &
\begin{tabular}{c} **Doppler Feature** \\ **DFSDF** \\ \end{tabular} & **Accuracy** \\ \hline P3 SF & ✓ & & & 93.6\% \\ ETFF & & ✓ & & 90.2\% \\ DFSDF & & & ✓ & 87.8\% \\ P3 SF and ETFF & ✓ & ✓ & & 96.6\% \\ P3 SF and DFSDF & ✓ & & ✓ & 95.6\% \\ ETFF and DFSDF & ✓ & ✓ & 96.1\% \\ All & ✓ & ✓ & ✓ & 97.1\% \\ \hline \hline \end{tabular} The checkmark symbol (✓) signifies that an option has been selected.  
\end{table} 
Table 5: The results of the ablation experiment. applied. This clearly indicates that the MFasNetV1 network proposed in this study is more suitable for few-shot learning scenarios. Finally, the inference capability of the networks was evaluated using the target task test set. The parameters [31; 32] and test accuracy of the proposed MFasNetV1 and the four lightweight networks are summarized in Table 6.  
In Table 6, MFasNetV1 has the highest test accuracy, followed by ResNet18, MobileNetV2, EfficientNetB0, and ShuffleNetV2x1 in descending order of performance. Upon further analysis, it is evident that the feature extraction networks of MFasNetV1 are optimized based on the characteristics of the feature spectrum, thereby enhancing its data representation capability.