## 4 Discussion
Additionally, the generalization ability of each feature extraction network was significantly improved through meta-learning training. The test results of the four lightweight networks indicate that ResNet18 demonstrates superior representation ability, better satisfying the requirements for representing time-frequency features (ETFFs) and Doppler features (DFSDFs). In terms of the number of parameters and FLOPs, MFasNetV1 ranks second only to ResNet18. However, the scale of each individual feature extraction network in MFasNetV1 is comparable to that of MobileNetV2 or ShuffleNetV2x1. The MFasNetV1 network can be readily deployed across multiple embedded AI chips and exhibits faster inference speeds through collaborative operations. The comparative experiments further confirm that the multidimensional feature fusion recognition method based on meta-learning, as proposed in this study, significantly enhances the adaptability to the targets' relative attitudes and improves recognition capability for moving targets. This approach offers an effective solution for addressing limited sample availability in active sonar applications. Specifically, within the context of active sonar target recognition discussed herein, the MFasNetV1 model demonstrates \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Module**} & \multicolumn{1}{c}{**Parameters (M)**} & \multicolumn{1}{c}{**FLOPs (M)**} & \multicolumn{1}{c}{**Accuracy (\%)**} \\ \hline \multirow{4}{*}{MFasNetV1} & HasNet-5 & 1.06 & 11.1 & \multirow{4}{*}{97.1} \\  & TFasNet-7 & 3.56 & 324.5 & \\  & DasNet-9 & 0.84 & 428.4 & \\  & FFC & 0.39 & 0.4 & \\ \hline \multirow{2}{*}{ResNet18} & ResNet18 & 11.68 & 1824.0 & 94.1 \\  & MobileNetV2 & 3.51 & 327.6 & 93.2 \\  & EfficientNetB0 & 5.29 & 412.8 & 92.7 \\  & ShuffleNetV2x1 & 1.26 & 146.0 & 91.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The parameters and test accuracy of the five networks. Figure 19: Validation curve for the feature-data-level fusion recognition network. superior performances compared to the data-level fusion methods employed in other network architectures, such as ResNet-18, MobileNetV2, EfficientNetB0, and ShuffleNetV2x1. In scenarios with extremely limited samples, in which rapid convergence necessitates a higher learning rate, the advantages of MFsNetV1 become particularly pronounced. These findings indicate that when addressing novel classification challenges, especially under conditions of severe sample scarcity, it is essential to leverage general network structures and design principles to develop specialized networks tailored to specific tasks. Toward the conclusion of the discussion, the results achieved in this study are presented in accordance with the stages of information fusion, as detailed in Table 7.  
Information fusion at different stages entails varying requirements for learning networks, training methods, and sample sizes, as well as differing issues to be resolved. Selecting the appropriate stage should also consider the project's potential needs. In addition, the comparative tests revealed that all network models exhibited a relatively high error rate when classifying samples affected by interface reverberation. The classification errors presented in Table 4 also corroborate this observation. Addressing the impact of reverberation on target recognition may constitute an important direction for future research.