两种非高斯海洋环境噪声统计模型分析

海洋环境噪声是影响声呐工作的主要因素之一,噪声模型的选取十分重要,普遍采用的高斯噪声模型在很多情况下存在局限性。引出两种非高斯噪声模型匹配实际非平稳的海洋环境噪声:广义自回归条件异方差模型(Generalized Autoregressive Conditional Heteroscedasticity,GARCH)和双模模型,通过分析两种噪声的概率密度分布函数,并与高斯模型噪声和实测海洋环境噪声比较得出两种噪声模型的适用性。GARCH(1,1)模型通过调节参数可以吻合大部分浅海和深海海洋环境噪声,双模模型则只对浅海某些情况存在适用性,而对深海吻合性较差。两种噪声模型的统计特性分析表明它们可以适用于高斯噪声模型存在局限的非平稳环境下。

Analysis of two kinds of non-Gaussian ocean ambient noise statistical models

Underwater ambient noise is one of the main factors affecting sonar performance. The selection of noise model is very important. Gaussian noise model is widely used, but in many cases limitations exist. This paper introduces two kinds of non-Gaussian noise models matching to the actual non-stationary ocean ambient noise： Generalized Autoregressive Conditional Heteroscedasticity （GARCH） model and bimodal model. Through the analysis of the two noise model＇s probability density functions （PDF） and by comparing them with the Gaussian noise model and the measured underwater ambient noise, the applicability of the two models is clarified. GARCH （1,1） can be fit for the majority of shallow and deep ocean ambient noise by adjusting the parameters. The bimodal model fits for some conditions of shallow water, not for the deep water. The statistical characteristics of the two noise models indicate that they can be used in the non-stationary cases where the Gaussian model has limitations.