This dissertation proposes a new framework to Non-Field-of-view (NFOV) sound source localization and tracking in indoor environments. The approach takes advantage of sound signal information to localize target position through auditory sensors combination with other sensors within grid-based recursive estimation structure for tracking using nonlinear and non-Gaussian observations.
Three approaches to NFOV target localization are investigated. These techniques estimate target positions within the Recursive Bayesian estimation (RBE) framework. The first proposed technique uses a numerical fingerprinting solution based on acoustic cues of a fixed microphone array in a complex indoor environment. The Interaural level differences (ILDs) of microphone pair from a given environment are constructed as an a priori database, and used for calculating the observation likelihood during estimation. The approach was validated in a parametrically controlled testing environment, and followed by real environment validations. The second approach takes advantage of acoustic sensors in combination with an optical sensor to assist target estimation in NFOV conditions. This hybrid of the two sensors constructs observation likelihood through sensor fusion. The third proposed model-based technique localizes the target by taking advantage of wave propagation physics: the properties of sound diffraction and reflection. This approach allows target localization without an a priori knowledge database which is required for the first two proposed techniques.
To demonstrate the localization performance of the proposed approach, a series of parameterized numerical and experimental studies were conducted. The validity of the formulation and applicability to the actual environment were confirmed. / Ph. D.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/56892 |
| Date | 12 October 2015 |
| Creators | Takami, Kuya |
| Contributors | Mechanical Engineering, Furukawa, Tomonari, Parker, Robert G., Leonessa, Alexander, Burdisso, Ricardo A., Stilwell, Daniel J. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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