Tracking the underwater acoustic channel using two-dimensional frequency sampling

Rapidly fluctuating multipath arrivals along with unpredictable surface wave focusing events render the shallow water acoustic channel difficult to track using sparse or least-squared error (LSE) optimization techniques. This fundamental bottleneck is primarily due to the time-varying nature of the underlying distribution. In this work, we propose a complementary channel tracking technique that exploits the dual representation of the acoustic channel in the Fourier domain and employs two-dimensional frequency sampling using an application-inspired input dictionary. Specifically, we reformulate the time-varying channel tracking problem on a MIMO framework and design training symbols that sample the channel in its dual Fourier domain. Ground truths based on experimental field data are presented.