# Study on Active Tracking of Underwater Acoustic Target Based on Deep Convolution Neural Network
## 2 Kalman Filter Active Tracking Based on Deep Convolutional Neural Network
### The Structure of the DCNN Model  
In this article, we input the target data set into deep convolutional neural network (DCNN) for training. The basic CNN consists of three structures: convolution, activation, and pooling [23]. DCNN is usually composed of multiple above-mentioned structures connected before and after and adjusted within the layer. The three key features of CNN are the local acceptance area, weight sharing and downsampling process, which effectively reduces the number of network parameters and alleviates the over-fitting problem of the model [24]. Convolution is the most basic and most important level. Convolution operation can extract the features of the image [25]. Through the convolution operation, certain features of the original signal can be enhanced and noise can be reduced. Pooling layers can reduce the amount of data processing while retaining useful information, and sampling can obfuscate the specific location of features [26]. Pooling layers are generally divided into mean pooling and maximum pooling. The advantages of CNN are sharing the convolution kernel, no pressure on high-dimensional data processing, no need to manually select features, and training the weights, that is, the feature classification effect is good. The disadvantages are the need to adjust parameters, the need for a large sample size,and training is best to use GPU. According to the characteristics of the target time-frequency image, we design a DCNN model. Figure 4 shows the model structure. There are in total five convolutional layers in the DCNN model we designed, a fully connected layer and an output layer. As shown in Formula (7), The activation function we use is rectified linear unit (ReLU), and its function is to perform nonlinear mapping on the output of the convolutional layer. It is characterized by fast convergence and simple gradient finding, which can prevent the gradient from disappearing. \[\text{ReLU}(x)=\left\{ \begin{array}{ll}x&x>0\\ 0&x\leqslant 0\end{array} \right. \tag{7}\]  
Figure 5 shows the flow of the input data. The data flows into the 'convo1' convolution layer through the input layer, and then into the pooling layer. In this layer, there are eight convolution kernels. The size of the input data is 4096 \(\times\) 024, the size of the convolution kernel is 6 \(\times\) 6, and the padding is set to \"same\", the output feature map has the same size as the input data, and the output feature map has eight dimensions. We use 'ReLU' as the activation function. The convolution kernel of the pooling layer is 4 \(\times\) 4, the step is 1, and the image generated by the feature mapping after pooling is 1024 \(\times\) 256 \(\times\) 8, that is, the dimension is 8 and the size is 1024 \(\times\) 256. The data flow of the 'convo2' convolutional layer of the DCNN model is shown in Figure 6. The 'convo2' convolution layer takes the feature map output by the 'convo1' convolution layer as input, and the size is 1024 \(\times\) 256 \(\times\) 8. The size of the convolution kernel of the convo2 convolution layer is 5 \(\times\) 5, the number is 32, and the output feature map is 1024 \(\times\) 256 \(\times\) 32. The activation function also uses 'ReLU'. The convolution kernel  
Figure 4: Schematic diagram of DCNN structure. After five convolutional pooling of the input data, the final output is generated through a fully connected layer. Figure 5: Convo1 convolutional layer process. of the pooling layer is set to 4 \(\times\) 4, the step size is 1, and the image generated by the feature map after pooling is 256 \(\times\) 64 \(\times\) 32, that is, the dimension is 32 and the size is 256 \(\times\) 64. As shown in Figures 7-9, the data from 'convo2' passes through the 'convo3' convolution layer and the 'convo4' convolution layer, and then enters the 'convo5' convolution layer. Figure 8: Convo4 convolutional layer process. Figure 6: Convo2 convolutional layer process. Figure 7: Convo3 convolutional layer process. Figure 9: Convo4 convolutional layer process.