| As countries attach more importance to marine resources,underwater acoustic signal processing methods have become an important research direction in marine environmental information processing.Signal sparse representation can effectively separate real signals from noise.In this paper,FM underwater acoustic signals are taken as the research object.The sparse decomposition method is mainly used to filter and reduce the noise of underwater acoustic signals.And build a signal simulation analysis processing platform,combined with related signal filtering methods for application research on the platform.Due to periodic interference,random white noise,and reverberation interference in the ocean and various water resources environments,in order to extract more effective signals from underwater acoustic signals,it is necessary to improve the detection of signals under different strong interference backgrounds.ability.In order to solve the problem of relatively low signal-to-noise of underwater acoustic signals in the water resources environment,this paper conducts research on underwater acoustic signals based on sparse representation algorithm,proposes a method for reducing noise of underwater acoustic signals based on sparse decomposition,and applies this method In the acoustic fuze detection system,the main tasks are as follows:(1)This article first introduces the basic model of underwater acoustic signals,and discusses the related theoretical basis of sparse representation algorithms,signal sparse representation methods,and related algorithms for signal sparse decomposition and noise reduction.At the same time,the method steps research and basic theoretical derivation required for signal filtering are performed.Finally,a comparison experiment is performed on the simulation platform with the empirical mode decomposition noise reduction method and wavelet noise reduction method.The experimental results show that the signal sparse decomposition and noise reduction method performs better under a certain signal-to-noise ratio and can extract effective features of underwater acoustic signals.(2)Secondly,based on the underwater acoustic signal model,this paper proposes a signal sparse decomposition method for noise reduction.The cyclic shift is used to construct the dictionary matrix and the corresponding initial dictionary.The adaptive orthogonal matching pursuit algorithm is used to orthogonalize all the atoms selected in the dictionary matrix.Construct the original signal to achieve the purpose of signal filtering.Simulation experiments show that compressed sensing of underwater acoustic signals can effectively improve the signal-to-noise ratio of the signals,and can extract effective information of underwater acoustic signals under certain interference conditions.(3)Finally,the proposed algorithm is applied to the acoustic fuze signal simulation analysis and processing platform to verify the practicability and effectiveness of the algorithm.Labview virtual instrument software and NI data acquisition card were used to build an acoustic fuze signal simulation analysis and processing platform.This platform loads original waveform data and sets algorithmrelated parameters to verify the sparse decomposition-based underwater acoustic signal denoising method proposed in this paper.The research results show that the sparse decomposition-based underwater acoustic signal denoising method proposed in this paper can effectively filter and reduce underwater acoustic signals. |
