Machine learning of acoustic propagation models for sound aware autonomous systems

Abstract

Opportunities exist for autonomous underwater vehicles to optimize acoustic communications with knowledge of the sound channel, but implementation has traditionally been hindered by computationally expensive propagation models. This work addresses computational complexity by enabling machine learning to interpret environmental inputs to predict transmission loss (TL) outputs from a physics-based ray tracing model (BELLHOP). Feature representations to reduce bathymetric and sound speed profile (SSP) information through wavelet decomposition and empirical orthogonal analysis are explored, respectively. A decision tree machine learning algorithm is trained to learn the paths (depths and ranges) of acceptable transmission loss for a pair of acoustic modems within a given environment. Our test case is off the coast of Southern California, and SSPs are collected from a 3 year record sampled from an ocean mooring. Bathymetry data is based on high resolution multibeam data. TL field realizations are calculated as training and testing data for environments of varying bathymetry with frequency of 25kHz and range of 1km through the BELLHOP model. Results are shown with stationary and non-stationary emitters to demonstrate the effectiveness of this approach.