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Technical Program

Paper Detail

Paper:	SAM-P4.11
Session:	Sensor Array Processing
Time:	Thursday, May 18, 14:00 - 16:00
Presentation:	Poster
Topic:	Sensor Array and Multichannel Signal Processing: Adaptive beamforming and direction of arrival estimation
Title:	Underwater Noise Modeling and Direction-Finding Based on Conditional Heteroscedasticity Time Series
Authors:	Hadi Amiri, Hamidreza Amindavar, Amirkabir University of Technology, Iran; Mahmoud Kamarei, University of Tehran, Iran
Abstract:	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 PDF s 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.