Demonstration of passive acoustic detection and tracking of unmanned underwater vehicles

Submitted in partial fulfillment of the requirements for the degree of Master of Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2018In terms of national security, the advancement of unmanned underwater vehicle (UUV) technology has transformed UUVs from tools for intelligence, surveillance, and reconnaissance and mine countermeasures to autonomous platforms that can perform complex tasks like tracking submarines, jamming, and smart mining. Today, they play a major role in asymmetric warfare, as UUVs have attributes that are desirable for less-established navies. They are covert, easy to deploy, low-cost, and low-risk to personnel. The concern of protecting against UUVs of malicious intent is that existing defense systems fall short in detecting, tracking, and preventing the vehicles from causing harm. Addressing this gap in technology, this thesis is the first to demonstrate passively detecting and tracking UUVs in realistic environments strictly from the vehicle’s self-generated noise. This work contributes the first power spectral density estimate of an underway micro-UUV, field experiments in a pond and river detecting a UUV with energy thresholding and spectral filters, and field experiments in a pond and river tracking a UUV using conventional and adaptive beamforming. The spectral filters resulted in a probability of detection of 96% and false alarms of 18% at a distance of 100 m, with boat traffic in a river environment. Tracking the vehicle with adaptive beamforming resulted in a 6.2±5.7 ∘ absolute difference in bearing. The principal achievement of this work is to quantify how well a UUV can be covertly tracked with knowledge of its spectral features. This work can be implemented into existing passive acoustic surveillance systems and be applied to larger classes of UUVs, which potentially have louder identifying acoustic signatures.Support from the National Defense Science and Engineering Graduate Fellowship and Draper Labs Fellowship, as well as DARPA for the support of the Bluefin Sandshark unmanned underwater vehicle. This research was conducted with Government support under and awarded by DoD, Air Force Office of Scientific Research, National Defense Science and Engineering Graduate (NDSEG) Fellowship, 32 CFR 168a

An acoustic remote sensing method for high-precision propeller rotation and speed estimation of unmanned underwater vehicles

Author Posting. © Acoustical Society of America, 2020. This article is posted here by permission of Acoustical Society of America for personal use, not for redistribution. The definitive version was published in Journal of the Acoustical Society of America 148(6), (2020): 3942-3950, https://doi.org/10.1121/10.0002954.Understanding the dominant sources of acoustic noise in unmanned underwater vehicles (UUVs) is important for passively tracking these platforms and for designing quieter propulsion systems. This work describes how the vehicle's propeller rotation can be passively measured by the unique high frequency acoustic signature of a brushless DC motor propulsion system and compares this method to Detection of Envelope Modulation on Noise (DEMON) measurements. First, causes of high frequency tones were determined through direct measurements of two micro-UUVs and an isolated thruster 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 second harmonic, with sideband spacings at the propeller rotation frequency multiplied by the poles of the motor. In shallow water field experiments, measuring motor noise was a superior method to the DEMON algorithm for estimating UUV speed. In negligible currents, and when the UUV turn-per-knot ratio was known, measuring motor noise produced speed predictions within the error range of the vehicle's inertial navigation system's reported speed. These findings are applicable to other vehicles that rely on brushless DC motors and can be easily integrated into passive acoustic systems for target motion analysis.This work was supported by the Office of Naval Research (Award No. N00014-17-1-2474), DARPA, the Draper Fellowship, and the National Defense Science and Engineering Graduate Fellowship Program.2021-06-2