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  • Research Article
  • Open Access

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

EURASIP Journal on Advances in Signal Processing20062007:071528

https://doi.org/10.1155/2007/71528

  • Received: 8 November 2005
  • Accepted: 29 June 2006
  • Published:

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 noise may 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.

Keywords

  • Time Series
  • Shallow Water
  • Gaussian Noise
  • Quantum Information
  • Sonar

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Authors’ Affiliations

(1)
Department of Electrical Engineering, Amirkabir University of Technology, P.O. Box 15914, Tehran, Iran
(2)
Department of Electrical and Computer Engineering, University of Tehran, P.O. Box 14395-515, Tehran, Iran

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Copyright

© Amiri et al. 2007

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