## 1 Introduction  
Underwater acoustic target passive recognition is a technology that is used to recognize the target type through a sonar system based on target radiation noise. Generally speaking, different ship targets have different hull structures, mechanical vibration characteristics and propeller structures. These factors lead to differences in radiated noise. Furthermore, due to the difference in ship working conditions and the interference of time-varying and space-varying underwater acoustic channels and ocean noise, the ship's radiated noise collected by hydrophones is complicated and fuzzy. Complexity and fuzziness increase the difficulty of underwater acoustic target recognition. Therefore, improving the underwater acoustic target recognition performance of a sonar system can be difficult. The method based on artificial intelligence enables complex data modelling and is suitable for algorithm design in complex scenes. Several researchers have applied artificial intelligence to underwater acoustic target recognition. Nowadays, ship target classification and recognition methods based on artificial intelligence are mainly divided into two kinds. One is the method of traditional machine learning, and the other is the method of deep learning. Classical machine learning methods include feature extraction and classifier design. Researchers extract various features from the ship's radiated noise signal based on traditional methods, such as waveform structure features [1; 2; 3], frequency characteristics and time-frequency analysis [4; 5; 6; 7; 8; 9; 10; 11; 12], and auditory perception features [13; 14; 15; 16; 17]. Then, the extracted features are input into the traditional machine learning classifiers, such as the classifier based on the statistical analysis method or the classifier based on the simple neural network [18; 19]. Although traditional machine learning methods can complete some recognition tasks, recognition accuracy is limited by the complex underwater environment, diversification of ship target working conditions, feature extraction of excessive artificial intervention and simple classifier design. To solve these problems, researchers applied deep learning to underwater acoustic target recognition. Generally speaking, the underwater acoustic target recognition algorithm based on deep learning first inputs the primary features or original signals into the deep neural network. The neural network then learns from a large amount of data to generate high-level embedded representations. Finally, these embedded high-level representations are used for classification. In recent years, a large number of studies have been conducted on neural network structure [20; 21; 22; 23] and learning strategies [24; 25] based on simple feature input or raw signal input. Some studies have been conducted on the performance and integration form of multiple feature combinations [26; 27; 28] based on the combination feature method. Furthermore, there are some methods to study data enhancement [29] and data generation [30] for deep network training. The deep learning method is a data-driven technology. It learns feature extraction and target representation from training data, avoiding the inefficiency and information loss of manual feature extraction. However, due to the lack of underwater acoustic target data, there are still many problems in the application of deep learning for underwater acoustic target recognition tasks, which are worthy of further study. When deep learning is used to conduct features on limited data, the neural network may pay too much attention to information or noise irrelevant to target features but related to dataset attributes in the learning process, and the features extracted may only apply to the training dataset and lack interpretability. The random initialization of the neural network model parameters, however, introduces many uncertainties, which aggravates the models' over-fitting on the limited amount of data.