Information-driven Guidance & Control of Heterogenous Underwater Sensor Networks for Adaptive Target Detection
Abstract
The proposed research will develop and demonstrate an information-driven approach for detecting, classifying, reacquiring, and identifying unexploded ordnances (UXO) using heterogeneous multi-agent Navy systems comprised of sonar-equipped unmanned underwater vehicles (UUVs), subject to environmental variability and uncertainty. Rapid environmental assessment (REA), surveying, and target detection, classification, and reacquisition missions typically involve multiple sensing assets whose performance is greatly influenced by the local environmental conditions and by the position and orientation of the UUV relative to the target and the seafloor. At the same time, these missions require safe underwater navigation and exploitation of the underwater topography for accurate geo-referencing, and of four-dimensional geophysical fluid structures for minimizing the energy needed for propulsion. For example, bathymetric data can be used for tactical navigation, as well as for determining the optimal positioning and orientation of the active sonar to circumvent reduced performance due to bottom clutters and underwater terrain shadows. In many cases, the risk of collisions and energy consumption must be minimized while sensing objectives, such as bottom surveying and target classification and identification, must be simultaneously maximized. The proposed research will develop novel information-driven navigation algorithms based on nonparametric Bayesian models and computational geometry to improve overall tactical navigation and mission planning, while enabling longer and safer missions by minimizing energy consumption and risk of collisions.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 12, 2016
- Source ID
- N000141512160
Entities
People
- Silvia Ferrari
Organizations
- Duke University
- Office of Naval Research
- United States Navy