# Globally Conditioned Conditional FLOW (GCC-FLOW) for Sea Clutter Data Augmentation
## 4 Experiment Results  
### Sea Clutter Augmentation Using GCC-FLOW  
Figure 4 compares the magnitude histogram of the generated sea clutter data by the GCC-FLOW trained on the high sea state data and on the low sea state data, respectively, with the magnitude histograms of the original data. The data of length 131, 072, same as the training data, were generated and subjected to comparison: (a) compares data in the high sea state, and (b) compares data in the low sea state. As illustrated in histograms (a), (b), the histogram of the generated data was fitted to the histogram of the corresponding training data, thereby indicating that the model had successfully learned the characteristics of the distributions associated with high and low sea state, respectively. It is crucial not only to evaluate the magnitude histogram but also to ascertain whether the autocorrelation characteristics are analogous. Figure 5 compares the magnitude autocorrelation of the generated sea clutter data by the GCC-FLOW trained on the high sea state data and on the low sea state data, respectively, with that of the original high sea state and the low sea state data, where the magnitude autocorrelation is defined as  
\[\rho(s)=\frac{\sum_{l=1}^{L-s}(|x_{l}|-\overline{x})(|x_{l+s}|-\overline{x})}{ \sum_{l=1}^{L}(|x_{l}|-\overline{x})^{2}}, \tag{11}\]  
where \(\overline{x}=\frac{1}{L}\sum_{l=1}^{L}|x_{l}|\), \(s=0,\cdots,L\), and \(L\) is set to 512. In Figure 5, (a) is the autocorrelation of the high sea state and (b) is the autocorrelation of the low sea state. The original data pertaining to high sea states (a) are observed to exhibit autocorrelation with high frequency fluctuations, whereas the data associated with low sea states (b) demonstrate autocorrelation with low frequency fluctuations. A comparison of the autocorrelation of the generated data with that of the original data reveals that the generated data and the original data are essentially indistinguishable for each sea state (a) and (b), respectively. The GCC-FLOW is trained using 256 blocks of 512 consecutive samples (131,072 training samples in total) with the global condition variable \(\alpha\) set to 0. GCC-FLOW generates 262,144 consecutive samples, and the first 131,072 samples are used to plot the histogram and the autocorrelation function. To confirm that the generated samples have the same statistical properties at each time point, consecutive 10,000 samples are taken from the beginning (1\(\sim\)10,000), from the middle (130,001\(\sim\)140,000), and from the end (252,145\(\sim\)262,144). Figures 6 and 7 show the magnitude histogram and the magnitude autocorrelation of the generated sea clutter data using GCC-FLOW trained from the high sea state data and the low sea state data, respectively, at the three time points. In Figure 6, the magnitude histograms of generated data of the high sea state are plotted for 10,000 samples of three different time points (beginning 1-(a), middle 1-(b), and end 1-(c)), and the autocorrelations are presented for 512 samples at each time point, as illustrated in 2-(a), 2-(b), and 2-(c). The result for low sea states are also illustrated in Figure 7 in a manner analogous to Figure 6. Note that the characteristic of the high sea data (concentrated distribution and high frequency fluctuation in correlation) and the characteristic of the low sea data (spread distribution and low frequency fluctuation in correlation) are preserved in all time points. Figure 8 shows the performance of the proposed GCC-FLOW for the Grimsby #155 dataset, collected at different geometric locations from the Dartmouth dataset, in terms of histogram (1) and autocorrelation (2). A total of 262, 144 data points were generated by the trained model. The three time points are designated by the beginning (1\(\sim\)10,000), middle (130,001\(\sim\)140,000) and end (252,145\(\sim\)262,144) of the sequence.