Parameter Estimation for Class a Modeled Ocean Ambient Noise

A Gaussian distribution is used by all traditional underwater acoustic signal processors, thus neglecting the impulsive property of ocean ambient noise in shallow waters. Undoubtedly, signal processors designed with a Gaussian model are sub-optimal in the presence of non-Gaussian noise. To solve this problem, firstly a quantile-quantile (Q-Q) plot of real data was analyzed, which further showed the necessity of investigating a non-Gaussian noise model. A Middleton Class A noise model considering impulsive noise was used to model non-Gaussian noise in shallow waters. After that, parameter estimation for the Class A model was carried out with the characteristic function. Lastly, the effectiveness of the method proposed in this paper was verified by using simulated data and real data

Parameter Estimation for Class A Modeled Ocean Ambient Noise

A Gaussian distribution is used by all traditional underwater acoustic signal processors, thus neglecting the impulsive property of ocean ambient noise in shallow waters. Undoubtedly, signal processors designed with a Gaussian model are sub-optimal in the presence of non-Gaussian noise. To solve this problem, firstly a quantile-quantile (Q-Q) plot of real data was analyzed, which further showed the necessity of investigating a non-Gaussian noise model. A Middleton Class A noise model considering impulsive noise was used to model non-Gaussian noise in shallow waters. After that, parameter estimation for the Class A model was carried out with the characteristic function. Lastly, the effectiveness of the method proposed in this paper was verified by using simulated data and real data