Abstract
In view of the non-Gaussian of ocean ambient noise, the stable distribution is applied to the statistical modelling. Firstly, the one-to-one correspondence between the four parameters of stable distribution and the sample mean, variance, skewness and kurtosis are established according to physical meaning. Then, numerical simulations are conducted to analyze the suitability of stable distribution for non-Gaussian ambient noise. In the case of white noise interference, noise is divided into Gaussian state, leptokurtic, and platykurtic separately. The parameters of stable distribution are estimated by the sample quantile and characteristic function method jointly. The simulation results show that, in the Gaussian state, stable distribution is equivalent to normal distribution. As for leptokurtic distribution, stable distribution is much better than normal distribution, indicating absolute predominance in impulse-like data modeling. But it is not adaptive for low kurtosis state because its characteristic exponent can’t be bigger than two. Finally, the result is verified by ambient noise collected in three environmental conditions, such as quiet ambient noise, airgun interference noise and ship noise. In all three cases, stable distribution shows good adaptability and accuracy, especially for the airgun dataset it is far superior to normal distribution.
Highlights
In underwater acoustic signal processing, ocean ambient noise is often assumed to be Gaussian distribution
The two models showed better results than Gaussian model, but Gaussian-Gaussian mixture (GGM) was suitable for noise which is close to Gaussian and Middleton Class-A model was too strict to use
Guo[10,11] analyzed the data of a sea trial, and found that the noise spectrum level in the 100-1000Hz frequency band is close to Chi-square distribution with degree of freedom of 5~8
Summary
In underwater acoustic signal processing, ocean ambient noise is often assumed to be Gaussian distribution. In practice it often shows non-Gaussian statistical characteristics due to natural or man-made disturbance[1,2,3,4,5]. Bouvet[8] used Gaussian-Gaussian mixture (GGM) and Middleton Class-A models to describe underwater noise data. The stable distribution has attracted more and more attention It has been used in radar signal processing[12,13], speech noise modeling[14] and emerged in the field of underwater acoustic modeling[15,16], which is limited to the interference conditions or reverberation fields of shrimp
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