Estimation and interpretation of multipath channel activity using braiding techniques

Abstract

Robust underwater acoustic channel estimation is critical towards improving communications efforts and enhancing awareness of changing environments. To explore these channels in-depth, machine learning algorithms are developed through feature geometric representations, referred to as “braiding,” to interpret multipath ray bundles within shallow water acoustic channels in two ways. The first application of this work predicts the number of reflections an acoustic signal may undergo through the environment by applying known physical parameters and braid features. The second application explores the importance of a braid feature within the acoustic channel for estimation purposes by using braid path information. Three unique machine learning techniques are trained to predict the applications using a diverse set of shallow water acoustic channels generated through the BELLHOP model. Machine learning models developed for the applications demonstrate high testing accuracies with an accuracy of 86.70% in the first application and an accuracy of 99.94% in the second application. As a further demonstration, braid feature representations and model predictions are used for channel estimation and determining the number of reflections using SPACE08 field data.