The ability of traditional active sonar processing methods to detect targets is often limited by clutter and reverberation from ocean environments. Similarly, multipath arrivals from radiating sources such as ships and submarines are received at sensors in passive sonar systems. Reverberation and multipath signals introduce constructive and destructive interference patterns in received spectrograms in both active and passive sonar applications that vary with target range and frequency. The characterization and use of interference phenomena can provide insights into environmental parameters and target movement in conjunction with standard processing methods including spectrograms and array beamforming. This thesis focuses on utilizing the time-frequency interference structure of moving targets captured on sonar arrays to enhance the resolution and abilities of conventional sonar methods to detect and localize targets. Physics-based methods for interference-based beamforming and target depth separation are presented with application of these methods shown using broadband simulated array data.

Automatic speech recognition has become a standard feature on many consumer electronics and automotive products, and the accuracy of the decoded speech has improved dramatically over time. Often, designers of these products achieve accuracy by employing microphone arrays and beamforming algorithms to reduce interference. However, beamforming microphone arrays are too large for small form factor products such as smart watches. Yet these small form factor products, which have precious little space for tactile user input (i.e. knobs, buttons and touch screens), would benefit immensely from a user interface based on reliably accurate automatic speech recognition. This thesis proposes a solution for interference mitigation that employs blind source separation with a compact array of commercially available unidirectional microphone elements. Such an array provides adequate spatial diversity to enable blind source separation and would easily fit in a smart watch or similar small form factor product. The solution is characterized using publicly available speech audio clips recorded for the purpose of testing automatic speech recognition algorithms. The proposal is modelled in different interference environments and the efficacy of the solution is evaluated. Factors affecting the performance of the solution are identified and their influence quantified. An expectation is presented for the quality of separation as well as the resulting improvement in word error rate that can be achieved from decoding the separated speech estimate versus the mixture obtained from a single unidirectional microphone element. Finally, directions for future work are proposed, which have the potential to improve the performance of the solution thereby making it a commercially viable product.

Vertical line arrays (VLAs) deployed below the critical depth in the deep ocean can exploit reliable acoustic path (RAP) propagation, which provides low transmission loss (TL) for targets at moderate ranges, and increased TL for distant interferers. However, sound from nearby surface interferers also undergoes RAP propagation, and without horizontal aperture, a VLA cannot separate these interferers from submerged targets. A recent publication by McCargar and Zurk (2013) addressed this issue, presenting a transform-based method for passive, depth-based separation of signals received on deep VLAs based on the depth-dependent modulation caused by the interference between the direct and surface-reflected acoustic arrivals. This thesis expands on that work by quantifying the transform-based depth estimation method performance in terms of the resolution and ambiguity in the depth estimate. Then, the depth discrimination performance is quantified in terms of the number of VLA elements.