## 4 Experimental Results and Discussion
### Results and Discussions  
We evaluated the average classification performance with 1000 simulations using weak, strong, and all data. For weak target detection analysis, 512 weak data were selected from the lower half of all data based on SNR and 611 remaining data were used for strong data. In total, 1000 clutters in each ping were randomly selected from the experimental data because the number of clutters greater than the noise level was huge. Weak and strong clutters were selected by the same algorithm used for target echoes. Table 2 shows the classification performance over all experimental data, and Table 3 shows the classification for weak and strong signals. In Table 2, the proposed algorithm is effective in terms of both the classification percentage and false alarm percentage. In Table 3, feature vectors trained in Table 2 are tested for weak and strong signals, and the results are comparable to the results in Table 2. The classification ratio is almost the same, especially for weak target signals. This  
In order to analyze the influence of strong clutters, only strong clutters were selected and trained. Thereafter, the process was the same as in the case where all data was analyzed. In Table 4, the target classification rate is about 8% lower than the results of Table 1, and the false alarm ratio is similar to the results of Table 2. This result indicates that clutters for training should be selected according to clutter distributions of the experimental area for weak target classification; otherwise, the target classification performance can be degraded. Thus, even if there is no target in the sea, data acquisition with active sonar in the underwater environment is needed to accumulate clutter distributions. Table 4 shows the average results of the classification performance when only the strong clutters are trained. In Table 5, the weak and strong target classification percentages are 74.3% and 85.3%, respectively. This shows that the trained clutters affect target classification, so all of the clutters should be trained for classification performance improvement as mentioned above. Figure 9 shows a ping history (50 pings of accumulated data) display comparing the proposed and conventional algorithms. The conventional algorithm is employed by presetting a threshold at 3 dB and extracting all signals that show the shape of the energy distribution up to the local minimum, the form of the largest peak center, and the gradually decreasing shape around the peak. This conventional algorithm is currently being applied to sonar systems and may not be the existing state of the art. We consider, however, that it is effective to compare the performance of the proposed algorithm. The situation is that the target comes toward the ownship and turns to the left during the last 10 pings. In the proposed algorithm result, target echoes are well classified and displayed. In addition, the number of clutters is greatly reduced compared with the conventional algorithm. This result shows that the proposed algorithm has a better performance in terms of the classification rate and false alarms. It can also help sonar operators greatly when detecting a target manually. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{3}{c}{**Predicted Class**} \\ \hline  & & **Target** & **Clutter** \\ \hline \multirow{2}{*}{True Class} & Target & 87.7\% & 12.3\% \\  & Clutter & 0.47\% & 99.53\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification table of the proposed algorithm with the test data for 1000 simulations (trained for only strong clutters). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & & \multicolumn{3}{c}{**Predicted Class**} \\ \hline  & & & **Target** & **Clutter** \\ \hline \multirow{3}{*}{True Class} & \multirow{3}{*}{Weak} & Target & 95.7\% & 4.3\% \\  & & Clutter & 0.3\% & 99.7\% \\ \cline{1-1} \cline{2-5}  & & Target & 99.2\% & 0.8\% \\ \cline{1-1} \cline{2-5}  & & Clutter & 1.2\% & 99.8\% \\ \hline \hline \end{tabular}
\end{table}