Multisource DOA estimation in a reverberant environment using a single acoustic vector sensor

We address the problem of direction-of-arrival (DOA) estimation for multiple speech sources in an enclosed environment using a single acoustic vector sensor. The challenges in such scenario include reverberation and overlapping of the source signals. In this work, we exploit low-reverberant-single-source (LRSS) points in the time-frequency (TF) domain, where a particular source is dominant with high signal-to-reverberation ratio. Unlike conventional algorithms having limitation that such potential points need to be detected at “TF-zone” level, the proposed algorithm performs LRSS detection at “TF-point” level. Therefore, for the proposed algorithm, the potential LRSS points need not be neighbors of each other within a TF zone to be detected, resulting an increased number of detected LRSS points. The detected LRSS points are further screened by an outlier removal step such that only reliable LRSS points will be used for DOA estimation. Simulations and experiments were conducted to demonstrate the effectiveness of the proposed algorithm in multisource reverberant environments

ACOUSTIC LOCALIZATION TECHNIQUES FOR APPLICATION IN NEAR-SHORE ARCTIC ENVIRONMENTS

The Arctic environment has undergone significant change in recent years. Multi-year ice is no longer prevalent in the Arctic. Instead, Arctic ice melts during summer months and re-freezes each winter. First-year ice, in comparison to multi-year ice, is different in terms of its acoustic properties. Therefore, acoustic propagation models of the Arctic may no longer be valid. The open water in the Arctic for longer time periods during the year invites anthropogenic traffic such as civilian tourism, industrial shipping, natural resource exploration, and military exercises. It is important to understand sound propagation in the first-year ice environment, especially in near-shore and shallow-water regions, where anthropogenic sources may be prevalent. It is also important to understand how to detect, identify, and track the anthropogenic sources in these environments in the absence of large acoustic sensory arrays. The goals of this dissertation are twofold: 1) Provide experimental transmission loss (TL) data for the Arctic environment as it now exists, that it may be used to validate new propagation models, and 2) Develop improved understanding of acoustic vector sensor (AVS) performance in real-world applications such as the first-year Arctic environment. Underwater and atmospheric acoustic TL have been measured in the Arctic environment. Ray tracing and parabolic equation simulations have been used for comparison to the TL data. Generally good agreement is observed between the experimental data and simulations, with some discrepancies. These discrepancies may be eliminated in the future with the development of improved models. Experiments have been conducted with underwater pa and atmospheric pp AVS to track mechanical noise sources in real-world environments with various frequency content and signal to noise ratio (SNR). A moving standard deviation (MSD) processing routine has been developed for use with AVS. The MSD processing routine is shown to be superior to direct integration or averaging of intensity spectra for direction of arrival (DOA) estimation. DOA error has been shown to be dependent on ground-reflected paths for pp AVS with analytical models. Underwater AVS have been shown to be feasible to track on-ice sources and atmospheric AVS have been shown feasible to track ground vehicle sources