Underwater acoustic signal denoising method based on self-adaptive window filtering and wavelet threshold optimization

Abstract

The invention discloses an underwater acoustic signal denoising method based on self-adaptive window filtering and wavelet threshold optimization. The method comprises the steps: firstly, Gaussian / non-Gaussian pulse noise in an underwater acoustic channel is described by combining S[alpha]S distribution and a normal distribution model; a median filtering method based on a self-adaptive window is designed, the size of the filtering window is corrected according to the number of noise points in the window, and non-Gaussian pulse noise is restrained; then, based on an improved artificial bee colony method GDES-ABC, threshold parameters of a wavelet threshold denoising method are optimized, and the Gaussian noise suppression capacity is improved. According to the method, Gaussian / non-Gaussianpulse noise in a complex underwater acoustic environment can be effectively suppressed, the receiving capability of underwater acoustic communication signals such as 2FSK, QPSK and 16QAM is improved,and a relatively high output signal-to-noise ratio and a relatively high noise suppression ratio are obtained.

Description

technical field [0001] The invention belongs to the technical field of underwater acoustic signal denoising, and in particular relates to an underwater acoustic signal denoising method based on adaptive window filtering (AWFM) and wavelet threshold optimization (GDES) in a Gaussian / non-Gaussian impulse noise environment. Background technique [0002] Acoustic waves are widely used in the field of underwater communication. During underwater transmission and processing, the acoustic wave signal will be affected by underwater complex Gaussian / non-Gaussian pulse noise, resulting in degradation and distortion of the acoustic wave signal and a decrease in communication quality. Signal denoising technology is a signal processing method used to improve signal quality and reduce the impact of noise, and is widely used in underwater acoustic communication and other fields. [0003] For the sudden non-Gaussian impulse noise emitted by underwater seabed exploration, marine life, sea sur...

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Problems solved by technology

[0005] However, in the actual ocean background noise environment, both Gaussian and non-Gaussian impulse noise are included, the wavelet threshold denoising method based on intelligent optimization is mainly used for Gaussian noise processing, it is difficult to directly apply to the comprehensive processing of oceanic underwater acoustic noise, and there are still a lot of problems. The disadvantages are specifically reflected in: firstly, there is a lack of a unified general principle for establishing a threshold function, which makes it difficult to construct a threshold function; secondly, the determination of threshold parameters is an iterative process, usually reaching a suboptimal value rather than an optimal value; and with the method As the number of iterations increases, the diversity of the population decreases, and the above optimization method may fall into a local minimum

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