An algorithm for estimating the position of the laser beam by the component scattered in the atmosphere by a matrix photodetector against a background of Gaussian noise is developed. The algorithm is based on the Lehman-Scheffe theorem, which allows one to obtain effective estimates of the distribution parameters using complete sufficient statistics for reports of the observed implementation of scattered laser radiation. The obtained estimates provide the best quality of parameter estimation for any finite sample sizes and do not require additional studies

The article presents the results of the analysis of the properties of disordered structures in hydrodynamic acoustics, associated with the process of detecting physical phenomena and marine objects based on the results of their mechanical impact on the marine environment, in which acoustic vibrations propagate. If vortices, attractors, fractals arise as a result of complex interactions of forces of nature (upwellings, seiches, Coriolis forces, currents, convection flows, rotation of the Earth) and are essentially mechanical effects on the environment of formation and propagation of an acoustic field, then mechanical sources of sound introduced into the hydrosphere (water) should repeat fractal iterations on a smaller scale at the sound field level. Recognizing the equations of hydrodynamics (the equation of motion, the equation of continuity, and the equation of state) as the fundamental equations of hydroacoustics, the nonlinearity of these equations is proposed to be considered the theory of the hydroacoustic field as nonlinear, and the linearity of the processes in this study is considered a special case. The principle of superposition also becomes a special case, and the Fourier transform, remaining necessary, loses its sufficiency. Fractal analysis in combination with wavelet analysis should be involved to help him

The study of the vibration parameters of ship structures is important for developing measures to ensure their reliable operation on ships. The commonly used analysis of vibrograms using the Continuous Fourier Transform (CFT) to accurately represent non-stationary functions in general and noise source signals in particular is unsuitable due to a number of drawbacks. The problems of spectral analysis and time-limited signal synthesis can be partially solved by switching to the Window Fourier Transform (WFT). The disadvantage of the WFT is that its calculation uses a fixed window, which cannot be adapted to the local properties of the signal. In order to get rid of this shortcoming for the analysis of vibrogram you can use wavelet transform. It also solves a number of other problems related to the processing of a noise signal. The word “wavelet” means small waves following each other (some sources have introduced the concept of “splash”). In a narrow sense, wavelets are a family of functions obtained by scaling and shifting a single, parent function. In a broad sense, wavelets are functions with frequency localization, whose average value is zero. The article shows the signs of a wavelet. Examples of the most common wavelet functions are given. The use of wavelet functions is proposed not only on the basis of time, but also frequency transformations. The implementation of the algorithm for analyzing vibration measurement data is proposed. An example of vibration measurement data and the results of their processing based on frequency wavelet analysis are given

The study of the vibration parameters of ship structures is important for developing measures to ensure their reliable operation on ships. The commonly used analysis of vibrograms using the Continuous Fourier Transform (CFT) to accurately represent non-stationary functions in general and noise source signals in particular is unsuitable due to a number of drawbacks. The problems of spectral analysis and time-limited signal synthesis can be partially solved by switching to the Window Fourier Transform (WFT). The disadvantage of the WFT is that its calculation uses a fixed window, which cannot be adapted to the local properties of the signal. In order to get rid of this shortcoming for the analysis of vibrogram you can use wavelet transform. It also solves a number of other problems related to the processing of a noise signal. The word “wavelet” means small waves following each other (some sources have introduced the concept of “splash”). In a narrow sense, wavelets are a family of functions obtained by scaling and shifting a single, parent function. In a broad sense, wavelets are functions with frequency localization, whose average value is zero. The article shows the signs of a wavelet. Examples of the most common wavelet functions are given. The use of wavelet functions is proposed not only on the basis of time, but also frequency transformations. The implementation of the algorithm for analyzing vibration measurement data is proposed. An example of vibration measurement data and the results of their processing based on frequency wavelet analysis are given