## 5 Conclusions  
This study primarily focused on addressing insufficient adaptability to the relative attitude of targets and the limited availability of samples in the context of underwater moving target recognition. Systematic research was conducted on feature extraction in spatial, time-frequency, and Doppler domains, and the development of a multidimensional feature fusion target recognition network based on meta-learning was investigated. Through integrating range-dimension information into generalized MUSIC spectral estimations, a novel approach for obtaining the pseudo-three-dimensional spatial feature spectra of underwater targets was proposed, thereby enhancing the relative attitude adaptability of target recognition. The pseudo-WVD time-frequency spectrum was refined through smoothing and filtering using prior knowledge of the transmitted signal, strengthening the target's time-frequency features. The composition of Doppler components for moving targets was analyzed and a method for extracting Doppler frequency shift distribution features was introduced, which compensates for the limitations of time-frequency features in terms of applicability to the relative attitude of targets. A multidimensional feature fusion recognition network based on meta-learning, called MFsNetV1, was presented. Feature extraction networks were specifically designed according to the characteristics of spatial, time-frequency, and Doppler feature spectra. Experimental data augmented with simulation data were utilized to construct both the meta-learning sample library and the target task sample library. Meta-learning training and feature-level fusion learning for target tasks were conducted using the proposed networks, and ablation experiments were also conducted. Meta-knowledge of these three features was acquired via meta-learning, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Stage of Fusion** & **Module** & **Accuracy (\%)** \\ \hline \multirow{4}{*}{Data-level} & ResNet18 & 94.1 \\  & MobileNetV2 & 93.2 \\  & EfficientNetB0 & 92.7 \\  & ShuffleNetV2x1 & 91.2 \\ \hline Feature-level & MFsNetV1 & 97.1 \\ Decision-level & MFsNetV1 & 94.1 * \\ \hline \hline \end{tabular}  
* This value is derived from the intermediate outputs of MFsNetV1.  
\end{table} 
Table 7: Test results categorized according to fusion stages. improving the generalization capability of the feature fusion network for target tasks while reducing the demand for training samples. Decision-level fusion results were derived from MFasNetV1 intermediate outputs. Data-level feature fusion classification networks were constructed based on ResNet-18, MobileNetV2, ShuffleNetV2x1, and EfficientNetB0, and their performances were compared to those of the proposed MFasNetV1 network. The network inference results confirm two key findings: first, the extracted multidimensional features exhibit complementary attitude adaptability; second, the proposed MFasNetV1 network effectively addresses the challenges of few-shot learning and demonstrates superior performance in multidimensional feature information fusion. Given the relatively limited experimental data employed, further experimental data collection is necessary for more comprehensive validation and refinement of the algorithm. Conceptualization, X.L. and Y.Y. ; methodology, X.L. ; software, L.L., X.L. and L.S. ; validation, X.L., Y.Y., Y.H., X.Y. and J.L. ; formal analysis, L.L. and Y.H. ; investigation, X.L., X.Y. and Y.H. ; resources, X.Y. and L.S. ; data curation, L.S. and L.L. ; writing--original draft preparation, X.L. ; writing--review and editing, X.L., Y.Y., Y.H. and J.L. ; visualization, X.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data are contained within the article. The authors appreciate the language assistance provided by [PERSON] during the revision of this manuscript. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: