Passive acoustic tracking of an unmanned underwater vehicle using bearing-Doppler-speed measurements.

Tracking unmanned underwater vehicles (UUVs) in the presence of shipping traffic is a critical task for passive acoustic harbor security systems. In general, the vessels can be tracked by their unique acoustic signature caused by machinery vibration and cavitation noise. However, cavitation noise of UUVs is quiet relative to that of ships. Furthermore, tracking a target with bearing-only measurements requires the observing platform to maneuver. In this work, it is demonstrated that it is possible to passively track an UUV from its high-frequency motor noise using a stationary array in a shallow-water experiment with passing boats. The motor noise provides high signal-to-noise ratio measurements of the bearing, range rate, and speed, which we combined in an unscented Kalman filter to track the target. First, beamforming is applied to estimate the bearing. Next, the range rate is calculated from the Doppler effect on the motor noise. The propeller rotation rate can be estimated from the motor signature and converted to the speed using a pre-identified model of the robot. The bearing-Doppler-speed measurements outperformed the traditional bearing-Doppler target motion analysis: the bearing, bearing rate, range, and range rate accuracy improved by a factor of 2×, 16×, 3×, and 6×, respectively. Finally, the robustness of the tracking solution to an unknown vehicle model is evaluated.

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

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.