# Underwater Target Recognition Based on Multi-Decision
### Related Works  
Recently, CNN has proven its powerful capability in many fields, such as computer vision, nature language processing, and wireless physical layer [6; 7; 8]. Convolutional neural networks are deep feedforward neural networks that include operations such as convolution calculations, pooled sampling, and nonlinear activation [4; 9; 10]. Comparedwith the traditional feedforward neural networks such as MLP, three strategies in CNN make use of the spatial correlation of data which include weight sharing, local receptive field and down sampling. They reduce the risk of over fitting, the defect of gradient disappearance the complexity and parameter size of the network. However, they improve the generalization ability of the network. CNN was first proposed by [PERSON] [11] in 1990 and applied to the handwritten character detection system. In 2014, [PERSON] [12] proposed GoogleLeNet which introduced the inception module. Receptive fields of different sizes enhanced the adaptability of the network to scale. The improved version [13; 14] greatly reduces the parameter amount to enhance the nonlinearity of the network and speed up the calculation. The residual network was proposed by [PERSON]. He [15] in 2015 adopted the idea of Shortcut Connection (SC) to solve the problem of network degradation. After full investigation and experimental verification, CNN is very suitable for underwater target recognition.  
In addition, many effective and efficient DL-based schemes have been proposed for underwater target recognition. For example, [PERSON]. [16; 17] focused on underwater target recognition which have sufficient training samples. In the first step, the original audio was converted into LOFAR spectrum, and then GAN was used for sample expansion. In the second step, a 15% performance improvement could be obtained using convolutional neural networks (CNNs) for feature learning and classification when the number of samples was more sufficient. [PERSON]. [18] combined competitive learning with deep belief network (DBN) and proposed a deep competitive network that used unlabeled samples to solve small number of samples in acoustic target recognition. This method could achieve a classification accuracy of 90.89%. To address the negative impact of redundant features on recognition accuracy and efficiency, the authors in [19] proposed a compressed deep competition network which combined network pruning with training quantization and other technologies and could achieve a classification accuracy of 89.1%. Refs. [20; 21] proposed a new time-frequency feature extraction method by jointly exploiting the resonance-based sparse signal decomposition (RSSD) algorithm, the phase space reconstruction (PSR), the time-frequency distribution (TFD), and the manifold learning. At the same time, a one-dimensional convolutional auto-encoder-decoder model was used to further extract and separate features from high-resonance components, which finally completed the recognition task and achieves a recognition accuracy of 93.28%. In addition, Refs. [22; 23; 24] all used convolutional neural networks for feature extraction, but the application scenarios and the classifiers were different. Ref. [22] proposed an automatic target-recognition method of unmanned underwater vehicle (UUV), which adopted CNN to extract features from sonar images and used support vector machine (SVM) classifier to complete the classification. Ref. [23] aimed to study different types of marine mammals. It also used the CNN+SVM structure to complete the feature extraction and classification recognition task. It compared the two classification and multi-class task scenarios. Ref. [24] adopted the civil ship data set and exploited the framework structure of CNN+ELM (extreme learning machine) as the underwater target classifier, which improved the recognition accuracy. We can see that with the in-depth research of scholars, the recognition rate of underwater targets based on deep learning has gradually increased.