Classification of spherical objects based on the form function of acoustic echoes
This work addresses automatic recognition of spherical objects in sonar applications, but it is incremental as it applies existing methods to a specific domain.
The paper tackled the problem of classifying spherical objects based on filler material (water or air) using acoustic echoes, achieving classification by employing the Form Function as a descriptor with a Neural Network classifier and comparing it to SVM and other representations.
One way to recognise an object is to study how the echo has been shaped during the interaction with the target. 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. The 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. In 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. The 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.