# A Method for Underwater Acoustic Target Recognition Based on the Delay-Doppler Joint Feature
## 4 Experiment
### Data Introduction and Preprocessing  
The data used in this paper were sourced from the ShipsEar database, which can be consulted online at [[http://atlantic.uvigo.es/underwatemoise/](http://atlantic.uvigo.es/underwatemoise/)]([http://atlantic.uvigo.es/underwatemoise/](http://atlantic.uvigo.es/underwatemoise/)), accessed on 1 November 2023. The hydrophone that collected these data was moored on the seafloor and connected to an underwater buoy to ensure a vertical position, and the upper end was connected to a surface buoy [28]. The database comprises 11 distinct categories of ship radiation noise data, as well as a collection of marine environmental background noise data. The data are stored in audio format, and Figure 13 is a time domain waveform of the first 20 s of a passenger ship in this database. Since the collection time of each ship is different, the total data duration of each type of ship is also different. This paper uses data from the four longest collected ship types and marine environmental noise as targets. The recognition model trained using only the ShipsEar database was not convincing; so, the author's research group collected a set of radiated noise data from fishing boats, passenger ships, and motorboats to verify the effectiveness of the model. The data collection site is located at Jimiya Port in the West Coast New Area in Qingdao City. The port is busy with freight transportation and has a large number of different types of ships entering and exiting the port all day. When we collected these data, the wind and waves were low; so, the impact of marine environmental noise on the data is relatively small. Figure 14 shows images of some targets in the ShipsEar database and data collected by the research team. Table 2 shows the target types and data duration of the data used in this article. These targets have significant differences in size, making them suitable for analyzing the target recognition performance of Delay-Doppler features based on them. Figure 14: Photos of different ship types and ambient marine noise. Figure 13: Radiated noise data of passenger ship in the first 20 s.  
Figure 15 shows the preprocessing process of the original data in this paper, with the aim of obtaining Time-Frequency and Delay-Doppler features for target recognition and using these two features as training sets to train the recognition model (TF-DD-CNN). First, this paper frames the original data to expand the data volume. Second, FFT is used to analyze the frequency domain features of the processed data, combined with the time domain feature to form the Time-Frequency feature. Third, SFFT is used to analyze the Delay-Doppler feature of the data. Finally, this paper uses the Time-Frequency and Delay-Doppler features of the signal to create training data for the TF-DD-CNN. The ratio of the number of training and testing sets is set to 5:1. This research applies frame processing techniques to the original radiation noise data in order to enhance the training set. Figure 16 depicts the process of dividing the radiation noise signal into frames. The length of the radiated noise signal is denoted as n, data frame length (_DFL_) indicates the length of the intercepted signal, where _DFL_ < n, and frame shift (_FS_) indicates that every few groups of data start to intercept the next frame of data. In this paper, the original data are divided into two frames, and the radiation noise data are divided into one frame every two seconds. To ensure the seamless flow of information between neighboring data frames, the overlap rate of adjacent data will be maintained by 50%. To meet the need for a 50% overlap rate, the step size can be selected as half of the _DFL_; the frameshift is one second. Figure 17 is the time domain diagram of a four-frame signal of certain data, with one frame of data every two seconds and the step size set to one second so that the data of two adjacent frames has a 50% overlap rate.