Advances in passive acoustic detection, localization, and tracking applied to unmanned underwater vehicles

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2022.Detection, classification, localization, and tracking (DCLT) of unmanned underwater vehicles (UUVs) in the presence of shipping traffic is a critical task for passive acoustic harbor security systems. In general, vessels can be tracked by their unique acoustic signature due to machinery vibration and cavitation noise. However, cavitation noise of UUVs 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 highfrequency motor noise using a stationary array in shallow-water experiments with passing boats. First, causes of high frequency tones were determined through direct measurements of two UUVs at a range of speeds. From this analysis, common and dominant features of noise were established: strong tones at the motor’s pulse-width modulated frequency and its harmonics. From the unique acoustic signature of the motor, I derived a high-precision, remote sensing method for estimating propeller rotation rate. In shallow-water UUV field experiments, I demonstrated that detecting a UUV from motor noise, in comparison to broadband noise from the vehicle, reduces false alarms from 45% to 8.4% for 90% true detections. Beamforming on the motor noise, in comparison to broadband noise, improved the bearing accuracy by a factor of 3.2×. Because the signal is also high-frequency, 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 rate. Extrapolating multiple measurements from the motor signature is significant because Bearing-Doppler-RPM measurements outperform traditional bearing-Doppler target motion analysis. In the unscented Kalman filter implementation, the tracking solution accuracy for bearing, bearing rate, range, and range rate improved by a factor 2.2×, 15.8×, 3.1×, and 6.2× respectively. These findings are significant for improving UUV localization and tracking, and for informing the next-generation of quiet UUV propulsion systems