Data Driven Methods for Structure Learning in Underwater Acoustic Modeling
Abstract
Traditional ocean acoustic modeling and array signal processing are key ingredients in a host of Naval concepts of operations. The" last decade has witnessed unprecedented advances in machine learning, data science, and signal processing leading to powerful, data-driven computationalparadigms for building models that capture complex structure in high-dimensional data, holding the promise of similar gains in performance and new capabilities in under-water acoustics. Only by coupling ocean physical models into these powerful data-driven approaches can these performance gains be realized in a manner that is robust to the variability and uncertainty of the ocean environment, as this research will emphasize. This effort aims at developing advances in these tools for the ocean acoustic environment, considering the unique characteristics of such environments and the capabilities that are ultimately sought in them. This will involve the development of accurate performance predictions and guarantees, which are key to understanding their applicability in future applications. Importantly, this effort will include a critical evaluation of the resulting methods, quantifying gains and limitations, robustness, computational requirements, interpretability, versatility, and other metrics, relative to traditional" approaches.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 20, 2019
- Source ID
- N000141912662
Entities
People
- Andrew C Singer
Organizations
- Office of Naval Research
- United States Navy
- University of Illinois Urbana–Champaign