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.
Highlights
A passive sonar generally employs array processing techniques to resolve problems such as localization of targets [1, 2]
We offer this model for the underwater noise in passive sonar due to the facts that the commonly used model for environmental additive noise exhibits heavier tail than the standard normal distribution [9], and the conditional heteroscedasticity suggests a time series model in which time-varying variances are presented, that is, a more logical modeling for the dynamic of the additive noise [7]
In this paper, we propose to assume a conditional heteroscedasticity-based time series for underwater noise modeling and that can be used in the direction-finding approach for passive sonar
Summary
A passive sonar generally employs array processing techniques to resolve problems such as localization of targets [1, 2]. GARCH models account for two main characteristics: excess kurtosis; that is, heavy tailed probability distribution, and the volatility clustering; that is, large changes tend to follow large changes and small changes tend to follow small ones, compatible to a large extent to the additive noises in a natural environment We suggested this more realistic dynamic model for additive noise modeling in array signal processing [8]. We offer this model for the underwater noise in passive sonar due to the facts that the commonly used model for environmental additive noise exhibits heavier tail than the standard normal distribution [9], and the conditional heteroscedasticity suggests a time series model in which time-varying variances are presented, that is, a more logical modeling for the dynamic of the additive noise [7].
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