The Navy relies on physics-informed, passive approaches to learn about the regional oceanography at a receiver. Of particular interest are data-driven through-the-sensor signal processing approaches which exploit the dominant, acoustic energy radiated by uncontrolled, broadband surface vessel. These sources are used to estimate sea-floor depth, bottom parameters, and the sound speed structure in the water column. Near-term research contributions include, but are not limited to, understanding the information content of noise, influential oceanographic processes, source dynamics, and optimal sensor configuration. Long-term efforts include contributions to developing data-assimilation models including Bayesian approaches to estimating environmental parameters.
Gemba, K. L., Sarkar, J., Cornuelle, B., Hodgkiss, W. S., & Kuperman, W. A. (2018). Estimating relative channel impulse responses from ships of opportunity in a shallow water environment. J. Acoust. Soc. Am., 144(3), 1231–1244.
signal processing; acoustics; oceanography; inverse theory; physics; machine learning; detection; noise; sources of opportunity