Machine Learning Transmission Loss Predictions in Acoustic Field Experiments

Abstract

This study evaluates the capabilities of three distinct transmission loss (TL) models to use as an on-board decision aid to identify locations in depth and range for an autonomous underwater vehicle (AUV) and an uncrewed surface vehicle (USV) to improve acoustic communications. AUVs and USVs are computationally and resource limited vehicles that require fast in-situ decisions to reposition. This work utilizes an experiment in August 2023 off the coast of Southern California of an SV3 Wave Glider communicating with a REMUS 100 vehicle equipped with a 25 kHz acoustic modem. Observations of TL across a range dependent bathymetry are used to evaluate various decision aid models that can provide acoustical awareness for the vehicles. Models are based on: first, simple approximations of sound intensity that decreases at range; second, a ray-based physics model Bellhop; and third, a machine learning (ML) decision tree model trained off-board the vehicle using Bellhop simulations. Predictions from the ML model show good agreement with the observed field data with an overall accuracy of 85.42%. Although there are discrepancies between locations of acceptable TL and those observed by the vehicles, the ML model is able to perform as well as the Bellhop model while improving the prediction speed of TL at an individual range and depth from 764.23 to 0.57 ms.