## 1 Introduction  
As an important branch of general-purpose marine key technologies, various UATR systems have emerged in various countries around the world. Underwater target recognition is widely used in marine geological reconnaissance, underwater defense, and other fields [1]. Optimizing the sonar signal collection capability and improving the hydroacoustic signal processing and feature extraction methods, and thus enhancing the system target recognition accuracy, have been the main goals of researchers [2]. The traditional underwater target recognition system only performs simple processing of the sonar collection signal, and then the sonar operator judges the recognition of the characteristics of the hydroacoustic signal (timbre, fluctuation, and beat) based on his personal experience, which brings up the problems of low efficiency and accuracy that have plagued a generation of hydroacoustic scientists. UATR is divided according to the medium: optical imaging and sonar [3]. Sonar recognition is either active or passive and is used to identify objects at close range, but a complex environment results in low image contrast. In active sonar recognition, a sonar waveform signal is transmitted into the water and reflected from the target; then, the sonar system receives and processes the echoes. By contrast, passive sonar receives noise radiated by a target and then extracts the feature information for target recognition. Due to ship-radiated noise containing important, specific information, passive sonar is also widely used [4]. Underwater acoustic signals received by passive sonar contain considerable noise. Therefore, these signals need to be analyzed to extract their features. The DEMON method extracts the invariant features of a target from its radiated noise and is widely employed in passive sonar recognition [5]. After the features of line spectra in the target-radiated noise are analyzed, the target is recognized from the result. In the recent years, as the most popular deep-learning model, the deep neural network (DNN) has attracted the interest of scholars in the field of UATR [6]. [PERSON] et al. combined the auditory perception principle and CNN to propose an auditory perception-inspired deep convolutional neural network (ADCNN) [7], which used a CNN to extract features of different frequency components from signals and merged them at the fusion layer to achieve the classification of acoustic targets. [PERSON] et al. used the absolute values of matrix elements in the cross-spectrum density matrix (CSDM) to generate two additional matrices as input data and used them for training the CNN model [8; 9]. [PERSON] et al. combined the CNN architecture with a second-order pooling (SOP) and used a convolution layer to learn the local features of the data extracted by constant-Q transform (CQT), achieving an end-to-end network to accomplish the classification of underwater targets. [PERSON] et al. proposed a compound convolutional neural network based on the shared latent sparse (SLS) feature and deep belief network (DBN) [10; 11], using these two functions to learn about fringe-based sonar images to improve the accuracy of classification. [PERSON] et al. proposed a method based on a convolutional neural network with residual units to recognize a time-frequency image of ship-radiated noise [12]. [PERSON] et al. combined improved antinoise power-normalized cepstral coefficients (ia-PNCC) with a CNN and applied multi-titaper and normalized Gammatone filter banks to improve the antinoise capacity [13; 14; 15; 16]. Complex-valued neural networks can process the amplitude and phase of the spectrum simultaneously and have been used to process and analyze wireless, image, and audio signals. They have the potential to be used for multitarget ship recognition.