## 3 The Proposed Algorithm  
Generally, active sonar systems display signals exceeding a preset threshold by the sonar console operator from matched filter outputs for beamforming results. These signals are displayed cumulatively and the results revealed to the sonar console operator, who then performs target classification. Figure 3 shows a procedure of target classification by the sonar console operator using active sonar systems. The feature vectors were extracted from the beamforming output of the sonar system. Figure 4 shows the block diagram of the proposed algorithm. The target classification algorithm is performed as a classification-before-detection concept with the beamforming output. The results of this classification are displayed on the sonar console for each ping. These results are very important for automatic tracking and detection by the sonar operator. Data received from the sensor of the cylindrical hull-mounted array used the Hanning window during the beamforming process. The delay and sum beamforming was performed in the time domain while the interpolation rate was 32. Compensation for sensor position changes was done to reduce the influence of the movement of the ship during the beamforming phase. The change in position of the sensor array was recalculated from the ship's pitch and roll data. Therefore, beamforming was performed on the compensated position of the sensor array. In addition, the sound velocity value was used for beamforming. PNCC is used to extract features from the beamforming result. Compared to MFCC, PNCC is superior in feature discrimination in the sonar operation environment where ambient noise changes time varying to the characteristics of an underwater environment, unlike Gaussian noise [29]. Therefore, auditory features were extracted using PNCC, which is advantageous for the sonar operating  
Figure 4: The proposed classification diagram. Figure 3: Target classification procedure by the sonar console operator. environment. The results were classified using CNN, and the results of MFCC feature extraction were compared with those classified using CNN. Figure 5 shows the block diagram for comparison with PNCC. The CNN model in Figure 5 is the same as in Figure 4.  
The feature extraction example for the target data using MFCC and PNCC is shown in Figure 6. Figure 6a represents the beamforming output. In Figure 6a, a target exists between 2.7 and 2.8 s. Figure 6b is the feature extraction output of MFCC and Figure 6c is the feature extraction output of the PNCC. The feature extraction example for clutter data using MFCC and PNCC is shown in Figure 7. Figure 7a is the beamforming output. In Figure 7a, the target exists between 2.2 and 2.3 s. Figure 7b is the feature extraction output of MFCC and Figure 7c is the feature extraction output of the PNCC. Figure 5: The classification diagram for comparison considering MFCC as baseline. Figure 6: Feature extraction results for target: (**a**) time signal of target with ambient noise; (**b**) MFCC feature extraction result of target; (**c**) PNCC feature extraction result of target. According to the feature extraction results in Figures 6 and 7, it can be seen that the PNCC is more discernable than the MFCC. Comparing Figure 6b,c, it is possible to distinguish between frame indexes 270 to 280, which is the part where the target exists, as PNCC looks more discernable than MFCC. In addition, in Figure 7b,c, it can be seen that PNCC looks more discernable than MFCC between frame indexes 220 and 230, where the clutter is located. In Figures 6 and 7, the cepstrum index means the order of cepstrum coefficients for each frame. Coefficient is 13 th order, delta is 13 th order, and delta-delta is 13 th order, and therefore it has a total cepstrum index of 39 th order. Since the target data from the real sea environment is very small compared to clutter data, more target data are generated using the data augmentation technique.