## 1 Introduction
At the same time, the multilayer neural network and robust adaptive controller also optimizes the target-tracking control of the underdriven autonomous underwater vehicles (AUV) [17; 18], improving the anti-interference ability of underwater target recognition in the face of complex marine environments. Multidimensional fusion networks and the support vector machine (SVM) are also used for UATR tasks. This study applied the DEMON method to analyze underwater target-radiated noise to obtain line spectra containing the features to be used in recognizing a target. Due to the variability of the underwater environment, collecting representative high-quality data is challenging. A huge number of samples are necessary for a traditional deep neural network to do this. Therefore, the need for a network model that can operate from a small sample has become urgent. In practical applications, Doppler shifts [19], due to the relative velocity between the sonar platform and the target, cannot be ignored. Moreover, the distance between the platform and target and the environmental noise affects the signal-to-noise ratio (SNR). Due to environmental or human interferences, redundant spectrum lines may appear in the DEMON spectrum of the received radiated noise, or some spectrum lines may weaken or even disappear, so using a neural network requires excellent generalization. A Siamese network, used for classification and recognition of problems with small sample sizes, is a promising candidate. [PERSON] et al. used one for face verification and recognition [20]. The Siamese network is widely used for identification and classification problems with small samples. [PERSON] et al. effectively extracted a few samples' classification features effectively through this network and determined the relationships between the features. [PERSON] proposed a one-shot Siamese network called Siam-OS for fast and effective tracking of visual objects [21; 22]. In this paper, we designed a network model for measuring the similarity of underwater target samples based on twin network architecture and 1D-CNN. The model was trained with simulated virtual samples, and the trained model calculated the similarity between two samples to be measured, then determined whether the two samples were the same class of targets, which effectively solved the problem of small samples of underwater targets being difficult to identify. Firstly, the modeling and feature extraction methods of underwater target radiation noise were thoroughly studied in this paper, and seven types of underwater target samples were designed for the training of the network model, based on real data. Secondly, in order to evaluate the generalization ability of the designed network model, samples with different Doppler frequency bias samples, different signal-to-noise ratios, and a different number of interference spectral lines were designed, based on the above seven types of underwater target samples as the evaluation dataset of the network model. In the training and evaluation process, positive and negative sample pairs were input to the network, and the network extracted features of the sample pairs, calculated Euclidean distances, and achieved satisfactory target classification and recognition. The effects of Doppler shift, signal-to-noise ratio and mutual interference on the network were obtained by evaluating the network performance [23; 24]. Finally, in order to test the recognition capability of the network model, the network model was tested using simulation samples and fewer real underwater target samples. Traditional methods use multiple classes and a large number of samples to train the network. The parameters of underwater samples are diverse, but the state of the collected samples is very homogeneous. This article used a single state to train the network to recognize samples with different states and achieved better recognition results.