This paper presents an acoustic localization system for small and low-cost autonomous underwater vehicles (AUVs). Accurate and robust localization for low-cost AUVs would lower the barrier toward multi-AUV research in river and ocean environments. However, these AUVs introduce size, power, and cost constraints that prevent the use of conventional AUV sensors and acoustic positioning systems, adding great difficulty to the problem of underwater localization. Our system uses a single acoustic transmitter placed at a reference point and is acoustically passive on the AUV, reducing cost and power use, and enabling multi-AUV localization. The AUV has an ultrashort baseline (USBL) receiver array that uses one-way traveltime (OWTT) and phased-array beamforming to calculate range, azimuth, and inclination to the transmitter, providing an instantaneous estimate of the vehicle location. This estimate is fed to a particle filter and graph-based smoothing algorithm to generate a consistent AUV trajectory. We describe the complete processing pipeline of our system, and present results based on experiments using a low-cost AUV. To the authors' knowledge, this work constitutes the first practical demonstration of the feasibility of OWTT inverted USBL navigation for AUVs.

One of the long term goals of autonomous underwater vehicle (AUV) minehunting is to have multiple inexpensive AUVs in a harbor autonomously classify hazards. Existing acoustic methods for target classification using AUV-based sensing, such as sidescan and synthetic aperture sonar, require an expensive payload on each outfitted vehicle and post-processing and/or image interpretation. A vehicle payload and machine learning classification methodology using bistatic angle dependence of target scattering amplitudes between a fixed acoustic source and target has been developed for onboard, fully autonomous classification with lower cost-per-vehicle. To achieve the high-quality, densely sampled three-dimensional (3D) bistatic scattering data required by this research, vehicle sampling behaviors and an acoustic payload for precision timed data acquisition with a 16 element nose array were demonstrated. 3D bistatic scattered field data were collected by an AUV around spherical and cylindrical targets insonified by a 7–9 kHz fixed source. The collected data were compared to simulated scattering models. Classification and confidence estimation were shown for the sphere versus cylinder case on the resulting real and simulated bistatic amplitude data. The final models were used for classification of simulated targets in real time in the LAMSS MOOS-IvP simulation package [M. Benjamin, H. Schmidt, P. Newman, and J. Leonard, J. Field Rob. 27, 834–875 (2010)].

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One application for autonomous underwater vehicles (AUVs) is detecting and classifying hazardous objects on the seabed. An acoustic approach to this problem has been studied in which an acoustic source insonifies seabed target while receiving AUVs with passive sensing payloads discriminate targets based on features of the three dimensional scattered fields. The OASES-SCATT simulator was used to study how scattering data collected by mobile receivers around targets insonified by mobile sources might be used for sphere and cylinder target characterization in terms of shape, composition, and size. The impact of target geometry on these multistatic scattering fields is explored, and a discrimination approach developed in which the source and receiver circle the target with the same radial speed. The frequency components of the multistatic scattering data at different bistatic angles are used to form models for target characteristics. Data are then classified using these models. Classification accuracies were greater than 98% for shape and composition. Regression for target volume showed potential, with 90% chance of errors less than 15%. The significance of this approach is to make classification using low-cost vehicles plausible from scattering amplitudes and the relative angles between the target, source, and receiver vehicles.