# Underwater Target Recognition Based on Multi-Decision
## 5 Numerical Results  
### Source of Experimental Data  
The experimental data used in this article is divided into two parts: The first part of the underwater acoustic database is named ShipsEar [5], which was recorded by [PERSON] et al. in the port of Vigo and it is vicinity on the Atlantic coast of northwestern Spain. The second part is based on the four types of signals simulated by the ship radiated noise. By mixing with the audio No. 81-92 in the database which are treated as the pure marine environment background noise, the simulated actual ship radiated noise under different signal-to-noise ratios is obtained.  
Vigo Port is one of the largest ports in the world with a considerable cargo and passengers. Taking advantage of the high traffic intensity of the port and the diversity of ships, it can record the radiated noise of many different types of ships on the dock, including fishing boats, ocean liners, Roll-on/Roll-off ships, tugboats, yachts, small sailboats, etc. The ShipsEar database contains 11 ship types (marine environmental noise) and a total of 90 audio recordings in \"wav\" format, with audio lengths varying from 10 s to 11 min.  
By extracting and summarizing audios in the database, it is divided into four categories according to the size of the ship types collected which is shown in Table 3. In addition, the date and weather conditions of the collected audios, the coordinates and driving status of the ship's specific position, the number, depth and power gain of hydrophones,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Input Layer (1024) \(\times\) 64 \(\times\) 1**} \\ \hline Conv+ ReLU & Conv+ ReLU & Conv+ ReLU & Conv+ ReLU & MaxPool (3 \(\times\) 3) \\ (7 \(\times\) 7) \(\times\) 16 & (7 \(\times\) 5) \(\times\) 16 & (5 \(\times\) 5) \(\times\) 16 & (3 \(\times\) 3) \(\times\) 16 & Conv+ReLU \\ stride = 2 \(\times\) 1 & stride = 2 \(\times\) 1 & stride = 2 \(\times\) 1 & (1 \(\times\) 1) \(\times\) 16 \\  & & & & stride = 1 \(\times\) 1 \\ \hline \multicolumn{3}{c}{Fileter concatenation} \\ \hline  & ReLU+MaxPool (3 \(\times\) 3) & & \\ \hline Conv+ReLU(5 \(\times\) 5) \(\times\) 16 stride = 2 \(\times\) 1 & & \\ \hline MaxPool (3 \(\times\) 3) & & \\ \hline Conv+ReLU(5 \(\times\) 5) \(\times\) 16 stride = 2 \(\times\) 1 & & \\ \hline MaxPool (3 \(\times\) 3) & & \\ \hline Conv+ReLU (3 \(\times\) 3) \(\times\) 32 stride = 2 \(\times\) 2 & & \\ \hline MaxPool (3 \(\times\) 3) & & \\ \hline Flattern & & \\ \hline Dense (4) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The CNN network model parameters of measured dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Value** \\ \hline Optimizer & adam \\ Learning rate & 0.01 \\ Number of samples & 200 \\ Training round & 30 \\ Loss function & Cross entropy loss function \\ \hline \hline \end{tabular}
\end{table}
Table 2: CNN training, optimization hyperparameters.  
atmospheric and marine environmental data are also listed in detail. The information can be used as a reference in the study.  
Because of military security considerations in the field of underwater target recognition, military databases are mostly kept secret. However, due to the inconvenience of collection and the high cost of civil databases, there are few public civilian databases for researchers to use. After the emergence of the ShipsEar database, it has been used in the application research of ship radiated noise separation, denoising, classification, etc. It is also common to use this database to complete research in the field of deep learning [18; 19; 20; 21; 37; 38; 39].