## 1 Introduction
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Algorithm Type** & **Limitations or Unresolved Challenges** \\ \hline Parameter estimation algorithms & These algorithms exhibits limited environmental adaptability, restricted interference discrimination capability, and relatively low recognition accuracy. \\ \hline Target scale-recognition algorithms & Research on the fine scattering characteristics of targets has primarily concentrated on static targets and has not yet been applied to the recognition of moving targets. Highlight-based image recognition algorithms exhibit sensitivity to the relative attitude of targets. \\ \hline Time–frequency feature recognition algorithms & Algorithm verification predominantly depends on pool experiments or simulation data. A few researchers have utilized experimental data to validate the classification between moving targets and static objects. Nevertheless, the limitations regarding the algorithm’s applicability to the relative attitude of targets have not been adequately addressed. \\ \hline Doppler feature recognition algorithms & Micro-Doppler features in the radar domain have been extensively utilized for target recognition. In contrast, in the sonar domain, Doppler features are primarily employed for target detection and velocity estimation, whereas the acquisition of micro-Doppler signatures remains challenging. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of various algorithms. This study focuses on resolving the challenges of relative attitude adaptability and sample scarcity in underwater moving target recognition for active sonar. Systematic research is carried out on feature extraction, information fusion, and network models. The methods for extracting spatial, time-frequency, and Doppler features of underwater targets are improved, and a meta-learning-based recognition method for multidimensional feature information fusion is introduced. Specifically, our contributions are as follows:  
1. Based on the generalized MUSIC algorithm, a pseudo-three-dimensional spatial feature was constructed by integrating range-dimension information, thereby enhancing the adaptability of the algorithm to the relative attitudes of the targets. The pseudo-WVD time-frequency spectrum was filtered and smoothed using the time-frequency function of the transmitted signal, which improved the resolution for distinguishing between targets and interferences. A method for extracting the Doppler frequency shift distribution feature of underwater moving targets was proposed, demonstrating excellent identification capabilities for moving targets. This feature is complementary to the posture adaptability provided by the time-frequency spectrum. 2. Inspired by meta-learning network structures such as MAML [21,22], a multidimensional feature information fusion network model based on meta-learning was developed. This model consists of three base learners, a meta-learner, a target task network, and a multidimensional feature fusion classifier. In accordance with the characteristics of spatial, time-frequency, and Doppler feature spectra, three concise base learners were specifically designed to mitigate overfitting and non-convergence issues in small-sample scenarios. 3. The designs of the meta-learning task space and the network training methodology were refined. A meta-learning sample library based on simulation data was constructed. A target task learning sample library was established by integrating experimental data with simulation-enhanced data. The training and testing results demonstrate that the meta-learning feature-level information fusion network model developed significantly outperforms the data-level information fusion approaches implemented in four lightweight networks, such as EfficientNetB0.