Underwater noise modeling and direction-finding based on heteroscedastic time series

Authors:
Hadi Amiri;Hamidreza Amindavar;Mahmoud Kamarei
Affiliations:
Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran;Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran;Department of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2007

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Abstract

We propose a new method for practical non-Gaussian and nonstationary underwater noise modeling. This model is very useful for passive sonar in shallow waters. In this application, measurement of additive noise in natural environment and exhibits shows that noise can sometimes be significantly non-Gaussian and a time-varying feature especially in the variance. Therefore, signal processing algorithms such as direction-finding that is optimized for Gaussian noisemay degrade significantly in this environment. Generalized autoregressive conditional heteroscedasticity (GARCH) models are suitable for heavy tailed PDFs and time-varying variances of stochastic process. We use a more realistic GARCH-based noise model in the maximum-likelihood approach for the estimation of direction-of-arrivals (DOAs) of impinging sources onto a linear array, and demonstrate using measured noise that this approach is feasible for the additive noise and direction finding in an underwater environment.