Data-driven inversion in identifying and characterizing EM ducts within the marine atmospheric boundary layer
Abstract
Data-driven inversion in identifying and characterizing EM ducts within the marine atmospheric boundary layer The PI and his student have developed a data-driven, faster-than-real-time inversion framework to detect and characterize the presence of such ducting within novel MABL environments. This data-driven framework has been demonstrated on realistic surrogate data that is consistent with real-world contexts: yielding quick and accurate inversions for detection and characterization of MABL ducting. The goal of the proposed research is to build on the PI s new data driven framework to: 1) demonstrate its efficacy using real-world data; 2) optimize sampling strategies and MABL library organization; 3) test an extension of the method for use with clutter return data (i.e. as a novel refractivity from clutter (RFC) method); 4) extend the method even further, to work with fixed transmitter (Tx) / Receiver (Rx) scenarios; and to 5) enhance robustness of the method, by leveraging other EM sources and frequencies.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jun 03, 2016
- Source ID
- N000141612077
Entities
People
- Christopher Earls
Organizations
- Cornell University
- Office of Naval Research
- United States Navy