Beam-time delay domain deconvolved scheme for high-resolution active localization of underwater targets

No abstract

In 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.

Using passive acoustics to distinguish unmanned underwater vehicles from other marine traffic in a complex environment and tracking a vehicle to understand its intent is critical for harbor security. Ships and boats can be classified by their unique acoustic signature due to machinery vibration and detection of envelope modulation on (cavitation) noise. However, cavitation noise of unmanned underwater vehicles is quieter than these vessels, and bearing-only measurements using a stationary array are insufficient for tracking. Tracking accuracy depends on the quality of acoustic measurements, such as high SNR, and observability, estimating vehicle state from the data available. In this work, we demonstrate that it is possible to passively track a vehicle from high-frequency motor noise using a stationary array in a shallow water experiment with passing boats. Motor noise provides high SNR measurements of bearing, bearing rate, propeller rotation, and range rate that are combined in an Unscented Kalman Filter to track the vehicle. First, receiver operating characteristic curves are generated to evaluate detection and false alarms. Conventional beamforming is applied to estimate bearing and bearing rate. Range rate is calculated from the Doppler effect of the motor noise. Propeller rotation is estimated from sideband spacings in the motor signature. [Work supported by Draper, ONR, and DARPA.]