## 3 Results
If the stationary random process cannot be used as the model, then in case of the nearby passage of a vessel or a motor boat with distinct propeller cavitation, when the levels of noise change at intervals of about a few minutes by over 20 dB or more (see Figure 1), AGC can be used for \"stationarization\" of the noise. The choice of time constant for AGC system (in this research it is equal to 1 s and does not change over time) is determined by the duration of the expected signals and noises. Since AGC is not a non-linear mathematical operation, it is difficult to forecast changes of PDF at the output of the AGC system. Therefore, the experimental estimation of PDF at the output of the AGC system in real conditions is of high interest. During this research, AGC was implemented through the division of all signal sample values by corresponding values of signal \"quasi-envelope\". \"Quasi-envelope\" values were obtained via the square law detection method. For the ease of further analysis, after the application of AGC, the signal was normalized by the levels in such a way that its sample mean value \(m_{y}\) and sample variance \({s_{y}}^{2}\) were 0 \(\pm\) 0.0001 and 1 \(\pm\) 0.0001, accordingly (bringing to the level (0, 1)). Figure 2 (top) represents an oscillogram of the same signal (as in Figure 1) after AGC and normalization; the vertical axis scale is conventional. The oscillogram looks like a noise track without smooth maximums caused by the vessel and the motor boat. Figure 2: Oscillogram (top) and spectrogram (bottom) with a scale reflecting dependence of color on the levels of spectral components of the signal in dB at the output of program module that is implementing automatic gain control (AGC) and normalization of the signal by the level. The scale is conventional. In Figure 2 (bottom), there is a spectrogram (FFT size 1024, Hann window) of the signal in Figure 2 (top). Above the spectrogram, there is a scale which reflects the dependency of color on the levels of spectral components of the signal in dB; the scale is conventional. The interferential pattern in the spectrogram became less noticeable; low-frequency components became more visible. For the description of the signal after AGC and normalization, a model of quasi-stationary ergodic random process of second order with \(m_{y}=0\) and \({s_{y}}^{2}=1\) can be already used as an initial approach. Figure 3 shows the oscillogram of the more detailed signal fragment, estimation of the mean spectrum (FFT size 1024, Hann window), and estimation of the initial area of the time correlation function of given signal after AGC and normalization for the time interval of 550-552 s. The spectrum has a slope typical for underwater noise and smoothed peaks at the frequencies lower than 100 Hz; the vibration of ship engines causes these peaks. The same peaks also represent the extremes of the time correlation function. When it comes to estimation of the characteristics of an underwater communication system in real conditions, the evaluation of time variability of the PDF of statistically dependent (see correlation function in Figure 3) sampled values of underwater noise is an important problem. Some results of the experimental evaluation of the probability density of various noises are described in [8; 9; 10]. Theoretical aspects regarding the evaluation of probability density function according to the research data are described in [11; 12; 13]. In numerous papers it is assumed that the probability density distribution of sea noise is Gaussian. Figure 4 illustrates the estimation of time dependences of PDF w(y) for sampled values of the signal, shown in Figure 2 (top). The histogram method of overlapping consecutive signal fragments by 50%, T = 10 s length each, (i.e., for consecutive fs \(\times\) T = 8000 \(\times\) 10 = 80,000 dependent samples) was used for the estimation of the PDF. The majority of smoothed histograms shown in Figure 4 are visually similar to the Gaussian curve with the mean equal to 0 and variance equal to 1.