## 4 Experimental Results and Discussion
### Experiments Data  
As previously stated, the active sonar real sea trial data used in the experiments are based on the beamforming of the reflected signal in the East Sea of South Korea. The target size was less than 100 m in length and the depth of the East Sea of South Korea was about 500 to 3000 m. The transmitting signal is a linear frequency modulation (LFM) signal with a sampling frequency of 31.25 kHz, a center frequency of 3.9 kHz, a bandwidth of 400 Hz, and a pulse length of 50 ms. The real sea trial data consists of 128 beams for 360 degrees omnidirectional and has 29 ping data obtained by transmitting with a PRI of about 13 s. This sea trial environment is shown in Figure 10.  
Figure 9: The proposed CNN model structure.  
From the received data of 29 pings, 361 target data and 3351 clutter data were extracted for classification. The extracted data were above the noise level. The example of the target data from the beamforming output and spectrogram is shown in Figure 11. Figure 11a shows 4-s beamforming output data and Figure 11b shows a spectrogram of Figure 11a. In Figure 11a, the LFM signal reflected from the target exists between 2.7 and 2.8 s and in Figure 11b, the LFM signal reflected from the target exists between 2.7 and 2.8 s and a center frequency of 3.9 kHz. Feature information for target classification is extracted from the beamforming result in Figure 11a.  
The feature extraction process uses a total of 26 mel-scale filter banks considering only the frequency band up to 8 kHz. The 13 th MFCC and PNCC were extracted through DCT and lifting. To account for the change over time, delta and delta-delta MFCC and PNCC were obtained. In total, 39 feature vectors were extracted.  
As mentioned, it is difficult to collect target data from the real sea experiment. The number of target data is very small compared to clutter data, so target data are generated using the data augmentation technique. The data augmentation technique of the target data is shown in Figure 12. The position of the window (red square) on the feature extraction output is moving forward and backward in the frame (the same meaning as time). This means that the temporal position moves while  
Figure 11: Example of the target data spectrogram: (**a**) beamforming output; (**b**) spectrogram.  
Figure 10: The environment of the experiments.  
maintaining the characteristics of the target. In the experiments, 3610 target data were generated from 361 original target data.  
The generated target data using the data augmentation technique and clutter data are used as CNN inputs. Figure 13 shows the entire experimental process. In this figure, the proposed CNN model takes an image of size \(39\times 395\) as input and has 3 convolution layers and 2 outputs. In the proposed CNN model, the size of the convolution filter is fixed at \(3\times 3\), and maxpooling uses a \(2\times 2\) window. Moreover, the rectified linear unit is used as the activation function and the result is obtained using a softmax function in the last layer. The software used in the experiments is Python 3.6.0 with TensorFlow 2.0 and Keras 2.3.0. The hardware specifications in the experiments are as follows: graphic card is GeForce RTX 2080 Ti and CPU is AMD Ryzen 7 2700X (8 core processor).  
Table 1 lists CNN input data numbers of target, clutter, training and testing. Eighty percent of the total data were used for training, while the remaining 20% were used for testing.  
Figure 12: Target data generation of MFCC and PNCC output using data augmentation: (**a**) MFCC data augmentation; (**b**) PNCC data augmentation.  
Figure 13: Structure for the experiment.