Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.777726
Title: Object characterisation using wideband sonar pulses
Author: Dmitrieva, Mariia
ISNI:       0000 0004 7963 5008
Awarding Body: Heriot-Watt University
Current Institution: Heriot-Watt University
Date of Award: 2018
Availability of Full Text:
Access from EThOS:
Access from Institution:
Abstract:
Characterisation of objects in an underwater environment is challenging. Success in the task can be beneficial in a variety of scenarios, which include oil and gas pipe maintenance, archaeology, and assistance to general underwater object identification. This work focuses on object characterisation, providing a solution for material identification. To do this, one must sense the underwater environment for which there are several different ways. Some of the most popular rely on sonar images. These provide limited information about the objects, mostly the shape, size and distance to the object. The study of acoustic wave scattering over a wide frequency range provides more information about the targets characteristics. This work builds on the principles of sound scattering. An acoustic echo reflected from an object has a different pulse shape and frequency composition than its initial pulse. These changes in the pulse are due to the interaction of the sound wave with an object during the reflection process and the pulses interaction with the transmission medium. Study of the reflected pulse can provide information about physical properties such as size, material and shell thickness. The objects used in this work are limited to spherical shells made of a variety of materials, and filled with different liquids or air. The task of material identification is approached in two different ways. The first one is a machine learning based approach. The classification is not based on the object's shape, but on its physical properties including the composition material. Two approaches will be presented: one, where the spherical shell is described by the echo's representation in time frequency domain and one, where it is described by the form function. The objects are classified using a number of machine learning techniques including support vector machine, gradient boosting and neural networks. The machine learning approaches give good results for a number of tasks, but are not sufficient to distinguish between materials with similar properties, like water and salt water. An alternative solution is presented in this thesis, which identifies the filler and the shell materials separately. This material identification approach is based on the timing of the sound scattering components. The echo reflected from an object is formed by a number of processes. The information about these processes can be extracted from the echoes and used to identify the material. This approach does not require any training data and shows good results, which are demonstrated on both the simulated and experimental data. This work focuses on object characterisation, providing a solution for material identification using underwater acoustics and signal processing techniques.
Supervisor: Lane, David ; Brown, Keith ; Heald, Gary Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.777726  DOI: Not available
Share: