## 4 Experimental Results and Discussion
### Experiment Data  
In the experiments, ownship with active sonar, a single target, and a few surface ships in the vicinity were employed. The frequency range of the active system was 5-10 kHz and the pulse length was under 500 ms for CW and LFM transmissions. In order to test the performance, shuffle and split cross-validation was used. In total, 80% of the total data set were randomly selected for training and the remaining data were used for testing. This cross-validation was carried out 1000 times to evaluate the average detection performance.  
For each ping, clutter was extracted for all peak data above the noise level. We did not categorize clutters into similar groups; we simply focused on separating targets from clutters. In order to extract all peak candidates, we scanned each ping's matched filter output data in two dimensions. Since we knew the target position and moving route from the ship's log file, we labeled and extracted target echo areas manually from the 1123 ping data sets for an exact target position search. Because multipath echoes show target-like patterns in terms of shape and geographical position, we categorized the multipath echoes as clutters according to their time delay and the known target position. In addition, for an evaluation of weak target classification, we divided all extracted data into two groups as weak or strong. The weak signal group was defined as the lower half of an SNR-ordered list of the signals extracted, and the strong signal group was defined as the upper half. In Figure 7, scatter diagrams show that in-layer and cross-layer feature vectors can separate targets from clutter efficiently.  
As mentioned, one of the purposes of this study was to investigate whether it is possible to detect a weak target. Thus, we followed the history of the real target echo SNR, and when the SNR came down, we analyzed the effect of the feature vectors. In Figure 8, in-layer feature #1 is the feature value of the B layer near the peak of the echo. In-layer feature #2 is the feature value of the E layer region (edge area) furthest from the peak. Figure 8 shows that the energy of the edge region is already low, similar to the noise level, so there is little change in the target echo's SNR. Thus, in-layer feature #1 for the region where the echo energy is distributed is proportional to the SNR of target. Figure 8 shows that in-layer features have fewer components to separate for a weak target. In contrast, the figure  
Figure 7: Scatter diagrams with in-layer and cross-layer features. (**a**) Scatter diagram of in-layer feature (variance in B area) and cross-layer feature (ratio of median value between B and C area); (**b**) scatter diagram of in-layer feature (variance in D area) and cross-layer feature (ratio of median value between C and D area); (**c**) scatter diagram of in-layer feature (skewness in C area) and cross-layer feature (ratio of variance value between B and C area); (**d**) scatter diagram of in-layer feature (skewness in D area) and cross-layer feature (ratio of variance value between C and D area).  
shows that the peak area feature of cross-layer feature vectors does not change much when the SNR of the target goes down to the noise level. This means that the remaining portion of the energy still has some feature components to classify a target in the cross-layer features. Thus, cross-layer features can detect a weak target efficiently and constantly, making the target continuously detectable.