The reverberation is one of the main factors which constrain the performance of the active sonar system. How to make a model for the reverberation and effectively estimate its parameters are very important for reverberation suppression.
Recently, a new reverberation suppression method which is called fuzzy statistical normalization processing is introduced by researchers from the Institute of Acoustics of the Chinese Academy of Sciences. It is designed to minimize the nonstationarity and the nonhomogeneity of the sonar data, such as the lower shadow area noise and the strong reverberation.
For the active sonar, the lower shadow area noise refers to the lower power noise and the lower power reverberation in the region screened by an object.
Besides, the strong reverberation is caused by the interfering targets, such as isolated rock outcrops, wrecks, and coral reefs, and is defined as the extended upper tail of the output distribution in the absence of a target signal.
The new method of fuzzy statistical normalization processing is realized based on the fuzzy Rayleigh background set and the defuzzification operation using the alpha-cut method.
The Rayleigh reverberation arises from the multiple reflections, diffusions, or diffractions of the transmitted signal by the sea surface, bottom, and elementary volume elements, such as fish, bubbles, and other inhomogeneities.
An alpha-cut of a fuzzy set is the crisp set that contains all the elements whose membership degree being greater than or equal to alpha.
If the membership degree of a fuzzy variable belonged to the fuzzy Rayleigh set is smaller than alpha, it would be an outlier of the Rayleigh background set. These outliers are the interfering resources to the background parameter estimation and the target detection.
With the new method of fuzzy statistical normalization processing, the outliers will be rejected by the background set and the target detection performance will be improved.
Computer simulation has been made to find the influence of fuzzy statistical normalization processing on the distribution of sonar data and the constant false alarm rate detection.
The simulation results show that the method of fuzzy statistical normalization processing can suppress the interfering targets, increase the shape parameter value, and improve the performance of the shape parameter estimator and the constant false alarm rate detector.
XU Yanwei, YAN Shefeng, MA Xiaochuan, XU Da. Research on Strong Reverberation Suppression for High Resolution Active Sonar. Chinese Journal of Acoustics (Vol. 35, No. 4, pp. 371-383, 2016).
Institute of Acoustics, Chinese Academy of Sciences, 100190 Beijing, China