Parameter Estimation for Class A Modeled Ocean Ambient Noise

Xuebo Zhang, Wenwei Ying, Bo Yang


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.


characteristic function; class A; noise modeling;non-Gaussian noise; parameter estimation; quantile-quantile (Q-Q) plot

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Istepanian, R. & Stojanovic, M., Underwater Acoustic Digital Signal Processing and Communication Systems, ed. 1, Kluwer Academic Publishers, USA, 2002.

Zhang, X.B., Tang, J.S. & Zhong, H.P., Multireceiver Correction for the Chirp Scaling Algorithm in Synthetic Aperture Sonar, IEEE Journal of Oceanic Engineering, 39(3), pp. 472-481, Jul. 2014.

Zhang, X.B., Huang, H.N., Ying, W.W., Wang, H.K. & Xiao, J., An Indirect Range-Doppler Algorithm for Multireceiver Synthetic Aperture Sonar Based on Lagrange Inversion Theorem, IEEE Transactions on Geoscience and Remote Sensing, 55(6), pp. 3572-3587, Jun. 2017.

Chitre, M., Underwater Acoustic Communications in Warm Shallow waters Channels, Ph. D Dissertation, Electrical & Computer Engineering, National University of Singapore, Singapore, 2006.

Jiang, Y.Z., Ying, W.W, Zhang, S.X., Guo, G.H. & Li, C.J., Non-Gaussian Noise Model of Extremely Low Frequency Channel and Its Application, National Defense Industry Press, China, 2014.

Liu, Z.Q., Signal Detection in Non-Gaussian Reverberation Background, Master dissertation, College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin, China, Mar. 2008.

Li, C.P., Research on the Identification Method of Non-Gaussian ARMA Model Based on High-order Statistics, Master dissertation, National University of Defense Technology, Changsha, China, Jan. 2002.

Stein, D., Detection of Random Signals in Gaussian Mixture Noise, IEEE Trans. Inf. Theory, 41(6), pp. 1788-1801, Nov. 1995.

Miller, J.H. & Thomas, J.B., The Detection of Signals in Impulsive Noise Modeled as a Mixture Process, IEEE Trans. Commu., COM-24(5), pp. 559-563, May 1976.

Nikias, C.L. & Shao, M., Signal Processing with Alpha-stable Distribution and Application, John Wiley & Sons, Inc., USA, 1995.

Middleton, D., Statistical-physical Models of Urban Radio-noise Environments-part I: Foundations, IEEE Transactions on Electromagnetic Compatibility, EMC-14(2), pp. 38-56, May 1972.

Middleton, D., Procedures for Determining the Parameters of the First-order Canonical Models of Class A and Classs B Electromagnetic Interference, IEEE Transactions on Electromagnetic Compatibility, EMC-21(3), pp. 190-208, Aug. 1979.

Zhang, S.X., Xu, D. Y., Jiang, Y.Z. & Bi, W.B., Probability Model Identification for Amplitude of Extremely Low Frequency Atmospheric Noise, Journal of Applied Sciences Electronics and Information Engineering, 26(4), pp. 336-341, Jul. 2008.

Peebles Jr., P.Z., Probability Random Variables and Random Signal Principles, McGraw-Hill, USA, 1980.

Field, E. & Lewinstein, M., Amplitude-probability Distribution Model for VLF/ELF Atmospheric Noise, IEEE Transactions on Communications, 26(1), pp. 83-87, Jun. 1978.

Chrissan, D.A., Statistical Analysis and Modeling of Low-frequency Radio Noise and Improvement of Low-frequency Communications, PhD dissertation, Stanford University, USA, 1998.



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