Defining the Uncertainty of Electro-Optical Identification System Performance Estimates Using a 3D Optical Environment Derived From Satellite

摘要

Current United States Navy Mine-Counter-Measure (MCM) operations primarily use electro-optical identification (EOID) sensors to identify underwater targets after detection via acoustic sensors. These EOID sensors which are based on laser underwater imaging by design work best in "clear" waters and are limited in coastal waters especially with strong optical layers. Optical properties and in particular scattering and absorption play an important role on systems performance. Surface optical properties alone from satellite are not adequate to determine how well a system will perform at depth due to the existence of optical layers. The spatial and temporal characteristics of the 3d optical variability of the coastal waters along with strength and location of subsurface optical layers maximize chances of identifying underwater targets by exploiting optimum sensor deployment. Advanced methods have been developed to fuse the optical measurements from gliders, optical properties from "surface" satellite snapshot and 3-D ocean circulation models to extend the two-dimensional (2-D) surface satellite optical image into a three-dimensional (3-D) optical volume with subsurface optical layers. Modifications were made to an EOID performance model to integrate a 3-D optical volume covering an entire region of interest as input and derive system performance field. These enhancements extend present capability based on glider optics and EOID sensor models to estimate the system's "image quality". This only yields system performance information for a single glider profile location in a very large operational region. Finally, we define the uncertainty of the system performance by coupling the EOID performance model with the 3-D optical volume uncertainties. Knowing the ensemble spread of EOID performance field provides a new and unique capability for tactical decision makers and Navy Operations.