## 5 Conclusions  
In this paper, we studied whether target and clutter can be classified by feature extraction and CNN. The classification performance of the proposed algorithm was analyzed by applying data above the noise level without a preset threshold by the sonar console operator. As a result of the evaluation, it was confirmed that targets and clutter can be classified by the proposed algorithm in a real underwater environment. Therefore, the proposed algorithm is of potential use for classifying underwater targets and can be helpful to the sonar console operators. These results are based on data from sea experiments obtained by applying an actual active sonar system. Although the sea  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Precision** & **Recall** & **F-Measure** \\ \hline MFCC & 98.49\% & 97.23\% & 97.86\% \\ PNCC & 99.10\% & 98.62\% & 98.86\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Precision-Recall analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Feature Extraction**} & \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**Results of Classification**} \\ \cline{3-4}  & & **Target** & **Clutter** \\ \hline MFCC & Target & 97.234\% & 2.766\% \\  & Clutter & 1.493\% & 98.507\% \\ \hline PNCC & Target & 98.617\% & 1.383\% \\  & Clutter & 0.896\% & 99.104\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of classification.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **MFCC** & **PNCC** \\ \hline Sum of Multiplications and & & \\ Divisions & 13,010 & 17,516 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of computational demands.  
experimental data do not represent all the characteristics of the underwater environment, the possibility of applying the proposed algorithm to an active sonar system has been vindicated.  
This paper shows that the PNCC can be used as feature vectors while CNN can be used as a classifier that leads to a higher classification rate in an active sonar system. The classification results also indicate that the proposed approach is better than MFCC used as feature vectors and the proposed approach is better than using the spectrogram. The fact that this is possible without setting a threshold when classifying targets is of great help to the sonar console operators in performing ASW.  
The proposed algorithm can be applied to all signals above the noise level without a preset threshold by the sonar console operator, so signals that are ignored in the process using a preset threshold can be detected. It is also possible to classify targets in one ping without accumulation of pings. Therefore, the proposed algorithm can greatly improve the detection, tracking and classification capabilities of the sonar console operators. Since real sea trial data of active sonar systems operating on naval ships were used in real underwater environments, the proposed algorithm is applicable to real active sonar systems.  
In future work, the fusion of different feature extraction methods can be a useful approach for active sonar systems. Furthermore, data from real sea trials in more diverse environments will be useful to compare the classification rates for each case.  
Conceptualization and methodology, S.L.; software, Y.K.; validation, I.S. and J.S.; formal analysis, J.S. and Y.K.; resources, S.L.; data curation, S.L. and Y.K.; writing--original draft preparation, S.L. and I.S.; writing--review and editing, S.L. and D.S.H.; visualization, Y.K.; supervision, D.S.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.