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Formal Reports

Report of results of completed projects or major milestones either in scientific terms or in terms acceptable to a wider audience. Note: Unless linked to the full text, reports are only available to NATO member nations from designated distribution centres. 

Documents

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Deployable network setup for Remote Sensing Satellite Ground Sation (RSSGS): architecture and performance considerations Deployable network setup for Remote Sensing Satellite Ground Sation (RSSGS): architecture and performance considerations

Date added: 06/01/2007
Date modified: 06/22/2012
Filesize: Unknown

Deployable network setup for Remote Sensing Satellite Ground Sation (RSSGS): architecture and performance considerations. Berni, A. ; Merani, D.; Gorra, E.; Leonard, M.; Ranelli, P.; Sciarrone, M.; Stenvoll, R. NURC-FR-2007-010. June 2007.

This document reports on the trials performed with the NURC Remote Sensing Satellite Ground Station (RSSGS) in 2005, as part of project “Covert Remote Sensing in Support of Expeditionary Operations” funded by Allied Command Transformation with the objective of demonstrating and evaluating the utility of remotely sensed METOC data to support expeditionary operations under the Recognized Environmental Picture (REP) concept. The experiment provided proof of concept and enabled the development of procedures and communications infrastructures that will enable future deployments of the RSSGS. Additionally, the application-level performance of the RSSGS communication system in different configurations was tested and validated.

SUPREMO v.2.0 User Guide SUPREMO v.2.0 User Guide

Date added: 06/01/2007
Date modified: 06/22/2012
Filesize: Unknown

SUPREMO v.2.0 User Guide. Prior, M.K. NURC-FR-2007-009. June 2007.

A user guide for version 2.0 of the SUPREMO multistatic sonar performance model is provided. Input data entry and model execution are explained in a step-by-step manner and the display of model predictions is described. Examples of automated program execution are given, showing how the model may be used to assess the impact of environmental input data uncertainty and to help in tactical considerations such as the identification of best deployment depths for sonars.

Stochastic nature of physical parameterizations in ensemble prediction: a stochastic convection parameterization Stochastic nature of physical parameterizations in ensemble prediction: a stochastic convection parameterization

Date added: 06/01/2007
Date modified: 06/22/2012
Filesize: Unknown

Stochastic nature of physical parameterizations in ensemble prediction: a stochastic convection parameterization. Teixeira, J.; Reynolds, C.A.  June 2007.  NURC-FR-2007-012. 

The goal of the ‘Air-Sea Interaction Effects on Expeditionary Force Operations’ project is to improve littoral METOC forecast capabilities to support Expeditionary Operations (EO) and to be used in Tactical Decision Aids (TDAs). Environmental forecasting systems often possess a substantial amount of uncertainty. A more refined knowledge of the uncertainty associated with the forecast of any variable would be of great help to support NATO operations. In order to answer this type of questions ensemble prediction systems have been developed to produce estimates of the uncertainty of model forecasts. The development of stochastic parameterizations is a fundamental stepping-stone for improved ensemble prediction systems. In this report it is argued that ensemble systems can be devised, in which parameterizations of sub-grid scale motions are utilized in a stochastic manner. The equations used to estimate the variance of a generic variable are discussed and a simplified algorithm is proposed as a preliminary attempt. Results from the implementation of this stochastic scheme show that this method is able to produce a substantial ensemble spread. Simulations with initial-condition and stochastic convection perturbations show a promising increase in ensemble spread and decrease in the number of outliers. The research devoted to this stochastic scheme has matured to the point of operational transition. As an example, this method is currently being tested in the US Navy operational ensemble. These methods will also be tested in the high-resolution re-locatable system used at NURC, as part of an environmental prediction package for EO support in the context of the Recognized Environmental Picture (REP).

Remote deployment of NURC Ground Station (2-16 November 2005) Remote deployment of NURC Ground Station (2-16 November 2005)

Date added: 07/01/2007
Date modified: 06/22/2012
Filesize: Unknown

Remote deployment of NURC Ground Station (2-16 November 2005). McCarthy, E.; Stenvoll, R.; Turgutcan, F.; Ranelli, P. NURC-FR-2007-013. July 2007.

The NURC Remote Sensing Ground Station was deployed over a 2 week period in November 2005. The antenna was dismantled at the NURC site and transported by flatbed truck to a remote site at an Italian military installation in Aulla, Italy where it was re-assembled. Within 24 hours of re-assembly, it was fully operational. All scheduled passes were successfully down linked. Over 20 passive and active images were received in real-time from several satellites including RADARSAT, TERRA, AQUA and ENVISAT. The Ground Station operated without event from 10-16 November. It was then dismantled within 24 hours and re-assembled at NURC. The remote deployment of such large equipment (the antenna is 5.4 meters in diameter) requires a significant amount of planning. To this end, this report details for potential users the effort required to remotely deploy the ground station, the logistics and support required, as well as the processes. This includes site preparation, power requirements, environmental conditions, and communications necessary.

Analysis of model-based automatic target recognition using the statistical theory of shape Analysis of model-based automatic target recognition using the statistical theory of shape

Date added: 08/01/2007
Date modified: 06/22/2012
Filesize: Unknown

Analysis of model-based automatic target recognition using the statistical theory of shape. Myers, V.; Couillard, M. NURC-FR-2007-016. August 2007.

This report investigates the use of shape theory in automated target recognition (ATR). The acoustic shadows cast by proud or partially buried objects on the seafloor are compared to theoretical shadows obtained with a ray tracing model. The shape of these shadows is treated as a mathematical object and statistical tools are used to extract information from a large dataset obtained during the Citadel sea trial in October 2005. Results are obtained for ATR with nearest neighbour and support vector machine classifiers using a feature which is the Procrustes shape similarity distance between a measured shadow and one obtained by the ray-trace model. The Hausdorff and Hamming distance measures are added as features to improve classification performance. Then, some hypothesis testing using the multivariate T2 test is performed to analyse populations of shapes of the shadows of truncated cones and rocks. The tests show that the mean measured rock and truncated cone shapes are statistically different from each other; however the same is true for the measured and modelled populations of cones . This multivariate test also underlines the inherent difficulties of comparing real images to modelled ones.

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