This thesis describes the challenges involved in detecting sources of acoustic noise using an autonomous underwater vehicle (AUV) in real world environments. The initial part of this thesis describes the developments made for redesigning an acoustic sensing system that can be used to estimate the relative bearing between a source of acoustic noise and an AUV.
With an estimate of the relative bearing, the AUV can maneuver toward the source of noise.
The class of algorithms that are used to estimate bearing angle are known as beamforming algorithms. A comparison of the performance of a variety of beamforming algorithms is presented. When estimating the bearing to a source of noise from a small AUV, the noise of the AUV, especially its propulsor, pose significant challenges. Toward the goal of active cancellation of AUV self-noise, we propose placing an additional hydrophone inside the AUV in order to estimate the AUV self-noise that appears on the exterior hydrophones that are used for bearing estimation. / Master of Science / A real world application using an autonomous underwater vehicle (AUV) is presented in this thesis. The application deals with detecting and estimating the relative location (bearing angle) between sources of acoustic noise and the AUV. The thesis starts by describing design changes made to target data sensing system inside the AUV for collecting and estimating the bearing angle. The estimation of bearing angle is done with a class of algorithms called beamforming algorithms whose performance comparison is presented on real world data.
Operating the AUV propulsor yields inaccurate bearing angle estimations and thus presents a huge challenge for bearing estimation. We propose measuring AUV self-noise using additional sensors to move towards the goal of cancelling AUV self-noise and recovering target signal for accurate bearing estimation.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/111165 |
| Date | 07 July 2022 |
| Creators | Murali, Rohit |
| Contributors | Electrical and Computer Engineering, Stilwell, Daniel J., Nazhandali, Leyla, Brizzolara, Stefano |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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