Supervised Machine Learning for Estimation of Target Aspect Angle From Bistatic Acoustic Scattering

When an aspect-dependent target is insonified by an acoustic source, distinct features are produced in the resulting bistatic scattered field. These features change as the aspect between the source and the target is varied. This paper describes the use of these features for estimation of the target aspect angle using data collected by an autonomous underwater vehicle (AUV). An experiment was conducted in November 2014 in Massachusetts Bay to collect data using a ship-based acoustic source producing 7-9-kHz linear frequency modulation (LFM) chirps insonifying a steel pipe. The true target orientation was unknown, as the target was dropped from the ship with no rotation control. The AUV Unicorn, fitted with a 16-element nose array, was deployed in data collection behaviors around the target, and the ship was moved to create two target aspects. A support vector machine regression model was trained using simulated scattering bistatic field data. This model was then used to estimate the target aspect angle from the data collected during the experiment. The difference between the estimates was consistent with experimental observations of relative source positioning. The simulation-based model appeared successful in estimating the target aspect angle despite uncertainties in target and source location and mismatch between true environment and simulation parameters.