## 1 Introduction  
The steadily increasing requirement for the specific weight of samples designed for automotive, aircraft and space engineering is one of the main reasons to replace metal parts with various composites (carbon-carbon, carbon fiber and fiberglass, woven, knitted, stitched, etc.) [1; 2; 3; 4; 5; 6]. However, the extensive application of composite parts for critical nodes subjected to constant loads requires continuous monitoring of their performance and fracture (without ceasing the operation of the structure during the repairs). The most common non-destructive testing method used to solve such problems is an acoustic emission method [7; 8; 9; 10; 11; 12]. This method records the elastic waves generated by local defects as they appear and grow in a loaded material. Analysis of acoustic emission data provides information on the activity, location and, in some cases, the type of an acoustic emission source [7; 10; 11; 12; 13]. Monitoring of structures by AE inevitably raises the question of how the useful signal can be separated from the interference of different nature. The sources of background noise (continuous or discrete) are as follows: the friction of the moving parts of the structure, the passage of technical liquids and gases through pipelines and channels, the flow of incoming air or liquid flow around critical nodes, electromagnetic interference, etc. Approaches to isolating a useful discrete acoustic emission (AE) signal (arising due to material fracture) against continuous or discrete noises can be divided into three types: spatial selection, parametric selection, and filtering. Spatial selection requires localization of acoustic emission sources with an acceptable error, which in the case of composite materials has proven to be a rather complex problem [14; 15; 16; 17]. Parametric selection is effective when the maximum amplitude of the acoustic emission pulses is, on average, at least 5 dB higher than the amplitude of the noise component. In the opposite case, the problem of original signal filtering has to be solved either to increase the signal-to-noise ratio or to separate the useful signal from the noise-like time-localized signals. The most common filtering methods are the windowed Fourier transform and discrete wavelet filtering. An important advantage of the wavelet transform is filtration of acoustic emission signals by the hard or soft threshold function (in terms of the given noise model) [18; 19; 20]. Although the efficiency of the wavelet transform has been already proved, it has some disadvantages. The main disadvantage is the dependence of filtering data and AE characteristics on the basic wavelet [21]. Alternative techniques for analyzing and filtering non-stationary signals are the empirical mode decomposition method and the Prony filtering method [22; 23; 24; 25; 26; 27]. Empirical mode decomposition method decomposes the signals (via an iterative procedure) into the sum of oscillating signals (empirical modes) selected in accordance with two specified requirements [23; 24; 25; 26; 27]. Unlike the wavelet transform, the basis functions are extracted directly from the examined signal with account of its local features. It is known that in some cases the time-frequency resolution of this method is better than the resolution achieved by use of the wavelet transform. Signal filtering is performed in the same way as in the wavelet transform; a certain threshold function is applied to the expansion coefficients in both cases [23; 24]. The Prony filtering method decomposes signal into a linear combination of exponential-cosine waves when finding a solution to the problem of residual function minimization. In order to effectively realize signal filtering, it is essential to find optimal decomposition parameters. For this purpose, the available information extracted from a wide range of different sources has been analyzed, which makes it possible to exclude the noisy part of the signal from consideration [22]. In ref.