Advances in Passive Acoustic Detection, Localization, and Tracking Applied to Unmanned Underwater Vehicles

Kristen Railey Kita, Ph.D., 2022
Henrik Schmidt, Co-Advisor
Dino DiBiaso, Co-Advisor
Detection, classification, localization, and tracking of unmanned underwater vehicles (UUVs) is a critical task for passive acoustic security systems. In general, vessels can be tracked by cavitation noise. However, UUV cavitation noise is considerably quieter than ships and boats, making detection significantly more challenging. In this thesis, I demonstrated that it is possible to passively track a UUV from its high-frequency motor noise using a stationary array in shallow-water experiments with passing boats. First, causes of tones were determined through direct measurements of UUVs. From the unique acoustic signature of the motor, I derived a high-precision, remote sensing method for estimating propeller rotation. In UUV field experiments, I demonstrated that beamforming on the motor noise, in comparison to broadband noise, improved the bearing accuracy by a factor of 3.2. The Doppler effect on motor noise is observable and I demonstrate that range rate can be measured. Furthermore, measuring motor noise was a superior method to the “detection of envelope modulation on noise” algorithm for estimating the propeller rotation. Bearing-Doppler-RPM measurements of the motor noise outperformed traditional bearing-Doppler target motion analysis. These findings are significant for improving UUV localization and tracking, and for informing the next-generation of quiet UUV propulsion systems.