tailieunhanh - Báo cáo hóa học: " Research Article Underwater Noise Modeling and Direction-Finding Based on Heteroscedastic Time Series"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Underwater Noise Modeling and Direction-Finding Based on Heteroscedastic Time Series | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 71528 10 pages doi 2007 71528 Research Article Underwater Noise Modeling and Direction-Finding Based on Heteroscedastic Time Series Hadi Amiri 1 Hamidreza Amindavar 1 and Mahmoud Kamarei2 1 Department of Electrical Engineering Amirkabir University of Technology . Box 15914 Tehran Iran 2 Department of Electrical and Computer Engineering University of Tehran . Box 14395-515 Tehran Iran Received 8 November 2005 Revised 29 April 2006 Accepted 29 June 2006 Recommended by Douglas Williams 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. Copyright 2007 Hindawi Publishing Corporation. All rights reserved. 1. INTRODUCTION A passive sonar generally employs array processing techniques to resolve problems such as localization of targets 1 2 . As a matter of fact all the DOA estimation methods make a crucial assumption for the noise model that have a great impact on the performance of DOA estimation. In the underwater environment the measurements .

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