Feature identification using acoustic signature of Ocean Researcher III (ORIII) of Taiwan
Authors: Fang Yin-Ying , Chen Chi-Fang , Wu Sheng-Ju 
Published: August 2019

Underwater acoustic signature identification has been employed as a technique for detecting underwater vehicles, such as in anti-submarine warfare or harbour security systems. The underwater sound channel, however, has interference due to spatial variations in topography or sea state conditions and temporal variations in water column properties, which cause multipath and scattering in acoustic propagation. Thus, acoustic data quality control can be very challenging. One of challenges for an identification system is how to recognise the same target signature from measurements under different temporal and spatial settings. This paper deals with the above challenges by establishing an identification system composed of feature extraction, classification algorithms, and feature selection with two approaches to recognise the target signature of underwater radiated noise from a research vessel, Ocean Researcher III, with a bottom mounted hydrophone in five cruises in 2016 and 2017. The fundamental frequency and its power spectral density are known as significant features for classification. In feature extraction, we extract the features before deciding which is more significant from the two aforementioned features. The first approach utilises Polynomial Regression (PR) classifiers and feature selection by Taguchi method and analysis of variance under a different combination of factors and levels. The second approach utilises Radial Basis Function Neural Network (RBFNN) selecting the optimised parameters of classifier via genetic algorithm. The real-time classifier of PR model is robust and superior to the RBFNN model in this paper. This suggests that the Automatic Identification System for Vehicles using Acoustic Signature developed here can be carried out by utilising harmonic frequency features extracted from unmasking the frequency bandwidth for ship noises and proves that feature extraction is appropriate for our targets.

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