Machine learning transmission loss simulations in complex undersea environments with range-dependent bathymetry

Abstract

Range-dependent bathymetric variability introduces diverse scattering and multipath that can impact optimal acoustic communication ranges, and will impact autonomous underwater vehicle (AUV) operations. We are working towards building a sound-aware framework in which the AUV has knowledge of the transmission environment and can exploit or react with intended outcomes. Prior knowledge of the environment coupled with acoustic propagation modeling on-board AUVs can improve sound-awareness of a vehicle but is computationally intensive, and costly from a power-budget perspective. This work addresses the computational burden by training a machine learning (ML) model to interpret range-dependent acoustic propagation through environmental inputs to predict transmission loss (TL) from the ray tracing model, BELLHOP. A decision tree model is trained to predict locations in range and depth of acceptable TL using feature representations to reduce bathymetric information within a region. Models are trained and tested with TL field realizations for acoustic communications at 10 and 25 kHz for environments with varying bathymetry at ranges up to 3 km through BELLHOP. We present simulations and results from field experiments performed off the coast of Southern California. The communication paths are between a surface vehicle (Liquid Robotics Waveglider, Herdon, VA) and an AUV (REMUS UUV, Huntington Ingalls, Falmouth, MA). Results for model predictions and collected field data will be discussed.