Unmanned underwater vehicles are becoming an increasingly important platform in oceanographic research and
operational oceanography, where continuous in situ sampling throughout the water column is essential to understanding
the ocean circulation and related biological, chemical, and optical activity. The latter directly affects field operations and
remote sensing capabilities from space. A unified approach is necessary for data quality control (QC), access, and
storage, considering the vast amount of data collected from gliders continuously deployed across large areas and over
long durations. The Binary Universal Form for the Representation of meteorological data (BUFR) maintained by the
World Meteorological Organization (WMO) is adapted to include physical and optical parameters from a variety of
sensor suites onboard underwater vehicles. The provisional BUFR template and related BUFR descriptors and table
entries have been developed by the U.S. Navy for ocean glider profile data and QC results. Software written in
FORTRAN using the ECMWF BUFRDC library has been implemented to perform both the encoding and decoding of
BUFR files from and to Network Common Data Form (NetCDF) files. This presentation also discusses data collected
from sensors on gliders deployed both in deep water and shallow water environments, including issues specific to optical
sensors at various depths.
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.
The Naval Oceanographic Office (NAVOCEANO) Glider Operations Center (GOC) supported its first joint-mission
exercise during Rim of the Pacific (RIMPAC) 08, a multi-national naval exercise conducted during July 2008 near the
Hawaiian Islands. NAVOCEANO personnel deployed four Seagliders from USNS SUMNER for Anti-submarine
Warfare (ASW) operations and four Slocum gliders for Mine Warfare (MIW) operations. Each Seaglider was equipped
with a Sea-Bird Electronics (SBE) 41cp CTD and Wet Labs, Inc. bb2fl ECO-puck optical sensor. The instrumentation
suite on the Slocum gliders varied, but each Slocum glider had an SBE 41cp CTD combined with one of the following
optical sensors: a Wet Labs, Inc. AUVb scattering sensor, a Wet Labs, Inc. bb3slo ECO-puck backscattering sensor, or a
Satlantic, Inc. OCR radiometer. Using Iridium communications, the GOC had command and control of all eight gliders,
with Department of Defense (DoD) personnel and DoD contractors serving as glider pilots. Raw glider data were
transmitted each time a glider surfaced, and the subsequent data flow included processing, quality-control procedures,
and the generation of operational and tactical products. The raw glider data were also sent to the Naval Research
Laboratory at Stennis Space Center (NRLSSC) for fusion with satellite data and modeled data (currents, tides, etc.) to
create optical forecasting, optical volume, and electro-optical identification (EOID) performance surface products. The
glider-based products were delivered to the ASW and MIW Reach Back Cells for incorporation into METOC products
and for dissemination to the Fleet. Based on the metrics presented in this paper, the inaugural joint-mission operation
was a success.
The effectiveness of sensors that use optical measurements for the laser detection and identification of subsurface
mines is directly related to water clarity. The primary objective of the work presented here was to use the optical data
collected by UUV (Slocum Glider) surveys of an operational areas to estimate the performance of an electro-optical
identification (EOID) Laser Line Scan (LLS) system during RIMPAC 06, an international naval exercise off the coast
of Hawaii. Measurements of optical backscattering and beam attenuation were made with a Wet Labs, Inc. Scattering
Absorption Meter (SAM), mounted on a Rutgers University/Webb Research Slocum glider. The optical data
universally indicated extremely clear water in the operational area, except very close to shore. The beam-c values from
the SAM sensor were integrated to three attenuation lengths to provide an estimate of how well the LLS would
perform in detecting and identifying mines in the operational areas. Additionally, the processed in situ optical data
served as near-real-time input to the Electro-Optic Detection Simulator, ver. 3 (EODES-3; Metron, Inc.) model for
EOID performance prediction. Both methods of predicting LLS performance suggested a high probability of detection
and probability of identification. These predictions were validated by the actual performance of the LLS as the EOID
system yielded imagery from which reliable mine identification could be made. Future plans include repeating this
work in more optically challenging water types to demonstrate the utility of pre-mission UUV surveys of operational
areas as a tactical decision aid for planning EOID missions.
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