UACE2017 Proceedings: Classification of spherical objects based on the form function of acoustic echoes

Session:
Towards Automatic Target Recognition. Detection, Classification and Modelling
Paper:
Classification of spherical objects based on the form function of acoustic echoes
Author(s):
Mariia Dmitrieva, Keith Brown, Gary Heald, David Lane
Abstract:
One way to recognise an object is to study how the echo has been shaped during the interaction with the target. Using wideband sonar allows the study of the energy distribution for a large range of frequencies. The frequency distribution contains information about an object, including its inner structure. This information is a key for automatic recognition. \nThe scattering by a target can be quantitatively described by its Form Function. The Form Function can be calculated based on the data of the initial pulse, reflected pulse and parameters of a medium where the pulse is propagating. \nIn this work spherical objects are classified based on their filler material – water or air. We limit the study to spherical 2 layered targets immersed in water. \nThe Form Function is used as a descriptor and fed into a Neural Network classifier, Multilayer Perceptron (MLP). The performance of the classifier is compared with Support Vector Machine (SVM) and the Form Function descriptor is examined in contrast to the Time and Frequency Representation of the echo.
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