Mathematical Modeling and Processing of Hydroacoustic Signals in Noise Environments
DOI:
https://doi.org/10.31861/sisiot2025.2.02019Keywords:
spectrum analysis, signal processing, acoustic signals, pulse response, scale parameterAbstract
The article presents the results of research into methods for improving the efficiency of hydroacoustic signal transmission in complex noise environments through the use of wavelet analysis, adaptive filtering, and mathematical modeling. Hydroacoustic systems are a key tool for underwater communication, but their operation is significantly complicated by the effects of multipath propagation, water turbulence, and reflections from the surface and bottom, which lead to distortion and reduced information reliability. The work implements a structural-functional synthesis approach that combines a mathematical description of signal propagation processes, wavelet decomposition for analyzing the time-frequency structure, and adaptive filtering based on the criterion of minimizing the root mean square error. The proposed model of the hydroacoustic channel takes into account the impulse response of the medium and additive white Gaussian noise, which allows for an adequate reproduction of real transmission conditions. To improve the accuracy of information reconstruction, a combined algorithm has been developed: at the first stage, wavelet decomposition of the received signal is performed, then threshold processing of detailed coefficients is carried out according to the Donohue rule, after which Wiener or LMS-type adaptive filtering is applied. Thanks to the time-frequency localization of wavelets, effective isolation of information components is ensured even at low signal-to-noise ratios. The obtained mean square error and signal-to-noise ratio parameters demonstrate a significant improvement in reconstruction quality. The proposed approach can be used in practical hydroacoustic systems to improve their noise immunity, stability, and accuracy of information retrieval in complex underwater conditions. The combination of wavelet transformation and adaptive filtering forms the basis for the creation of intelligent signal processing systems capable of ensuring effective operation in conditions of stochastic noise and uneven propagation of acoustic waves.
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