In this work, we explore machine learning through a model-agnostic feature representation known as braiding, that employs braid manifolds to interpret multipath ray bundles. We generate training and testing data using the well-known BELLHOP model to simulate shallow water acoustic channels across a wide range of multipath scattering activity. We examine three different machine learning techniques—k-nearest neighbors, random forest tree ensemble, and a fully connected neural network—as well as two machine learning applications. The first application applies known physical parameters and braid information to determine the number of reflections the acoustic signal may undergo through the environment. The second application applies braid path information to determine if a braid is an important representation of the channel (i.e., evolving across bands of higher amplitude activity in the channel). Testing accuracy of the best trained machine learning algorithm in the first application was 86.70% and the testing accuracy of the second application was 99.94%. This work can be potentially beneficial in examining how the reflectors in the environment changeover time while also determining relevant braids for faster channel estimation.
Autonomous learning and interpretation of channel multipath scattering using braid manifolds in underwater acoustic communicationsa)
Ryan A. McCarthy, Ananya Sen Gupta, Madison Kemerling; Autonomous learning and interpretation of channel multipath scattering using braid manifolds in underwater acoustic communications. J. Acoust. Soc. Am. 1 August 2021; 150 (2): 906–919. https://doi.org/10.1121/10.0005819
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