## 3 Results  
### Dataset  
The array element domain simulation data were generated based on the fundamental highlight models of the underwater vehicle and four types of interference, as illustrated in Figure 13a,b [9]. The active sonar array consists of 25 elements configured as [1 3 5 7 5 3 1], arranged at half the wavelength corresponding to the center frequency of 24 kHz. The transmitted signals include single-frequency tones and linear frequency-modulated waveforms, with a pulse duration of 70 ms. The relative horizontal orientation angles of the active sonar relative to the target's tail are set to 15\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\), and 90\({}^{\circ}\), while the relative pitch angle is fixed at 1.5\({}^{\circ}\). The distance between the sonar reference center and the geometric center of the target ranges from 350 to 450 m. Additionally, the sonar platform operates at a speed range of 25 to 35 knots, while the target proceeds at a speed range of 10 to 20 knots. Figure 11: Network architecture for target tasks. Figure 12: Architecture of MFasNetV1 and parameters of the feature fusion classifier. Figure 13: Highlight models: (**a**) underwater vehicles; (**b**) four types of interferences. Underwater moving targets generally exhibit a streamlined shape, and their feature spectra vary with the relative attitude [27]. In the meta-learning phase, different targets and their respective relative horizontal orientation angles are treated as distinct categories. Additionally, variations in relative horizontal orientation angles for the same target are also classified as separate categories. For example, given E types of targets and \(F\) types of relative horizontal orientation angles, this results in a total of \(E\times F\) unique categories. Assuming that one of the meta-learning classification task \(T_{i}\) involves five-class classification (n-way = 5), any five categories are randomly selected from a total of \(E\times F\) categories, denoted as \(C_{E\times F}^{5}\). During training for each task, a small number of samples (k-shot = 4) are utilized. Thus, the task space for meta-learning is constructed as \(\mathbf{T}=\{T_{1},T_{2},...,T_{M}\}\). The meta-learning sample library consists of the support and query sets. Based on the basic model presented in Figure 13, adjustments were carried out on the number of highlights, their intensity, and their distribution; this was designated as Parameter Configuration 1 (PC1). The settings of RHOA and RPA for generating samples in the support set are detailed in Table 2. The value of RHOA follows a Gaussian distribution with a mean specified in Table 2 and a variance of 1.5. In total, 30 combinations were constructed based on the target types and relative horizontal orientation angles, with 20 samples generated for each combination, resulting in a total of 600 samples. The sample generation method is based on approaches presented in [28]. Each of the 30 combinations was treated as a distinct target type. By setting the number of tasks, M, to 25,000 and the batch size, N, to 10, the meta-learning support set was established. Following the methodology and parameters used for generating the support set, adjustments were made to the number of highlights, their intensity, and their distributions; this was designated as Parameter Configuration 2 (PC2). Subsequently, 20 new samples were regenerated for each combination, forming the meta-learning query set. The target task sample library consists of a training set, a validation set, and a test set. Underwater vehicles are treated as true targets, whereas the four types of interferences (I-IV) are classified as false targets. Effective data were extracted from the experimental dataset, with homogeneous data excluded.