Underwater Noise Modeling and Direction-Finding Based on Conditional Heteroscedastic Time Series

In this paper, we propose a new method for practical non-Gaussian and non-stationary underwater ambient noise modeling and direction-finding approach. In this application, measurement of ambient noise in natural environment shows that noise can sometimes be significantly non-Gaussian and time-varying features such as variances. Therefore, signal processing algorithms such as direction-finding that are optimized for Gaussian noise, may degrade significantly in this environment. Generalized autoregressive conditional heteroscedasticity (GARCH) models are feasible for heavy tailed PDFs and time-varying variances of stochastic process and also has flexible forms. We use a more realistic GARCH (1,1) based noise model in the maximum likelihood approach for the estimation of direction-of-arrivals (DOAs) of impinging sources and show using experimental data that this model is suitable for the additive noise in an underwater environment

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