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The Multistatic Tactical Planning Aid (MSTPA) - user guide V4.1.2R The Multistatic Tactical Planning Aid (MSTPA) - user guide V4.1.2R

Date added: 05/22/2013
Date modified: 06/10/2013
Filesize: Unknown

The Multistatic Tactical Planning Aid (MSTPA) - User Guide V.4.1.2R. Strode, Christopher.; Oddone, Manlio CMRE-SP-2013-001. May 2013

This overview is designed to introduce the user to the major functionality of the Multistatic Tactical Planning Aid (MSTPA) in order to facilitate its use within both the scientific and operational maritime communities. The guide begins with installation instructions followed by a description of the major components of the input file format. This includes the environmental and propagation model details together with sensor and platform motion components. Example output file format is then described together with Matlab code to facilitate more detailed plotting and analysis functionality. The optimisation mode of the tool is introduced and described by means of simple example scenarios. Finally, the batch processing mode is described such that the core acoustic and contact generation modules may be used to generate results while suppressing any graphical output. This may be used to incorporate MSTPA into existing simulation frameworks.

NATO Undersea Research Centre Human Diver and Marine Mammal Risk Mitigation Rules and Procedures NATO Undersea Research Centre Human Diver and Marine Mammal Risk Mitigation Rules and Procedures

Date added: 09/01/2006
Date modified: 06/19/2012
Filesize: 304.56 kB

NURC-SP-2006-008. NATO Undersea Research Centre Human Diver and Marine Mammal Risk Mitigation Rules and Procedures. September 2006.

NATO Undersea Research Centre Human Diver and Marine Mammal Risk Mitigation Rules and Procedures (second edition) NATO Undersea Research Centre Human Diver and Marine Mammal Risk Mitigation Rules and Procedures (second edition)

Date added: 12/01/2008
Date modified: 06/19/2012
Filesize: 176.82 kB

NURC-SP-2008-003. NATO Undersea Research Centre Human Diver and Marine Mammal Risk Mitigation Rules and Procedures (second edition). December 2008.

NATO Undersea Research Centre Marine Mammal Risk Mitigation Rules and Procedures NATO Undersea Research Centre Marine Mammal Risk Mitigation Rules and Procedures

Date added: 11/01/2009
Date modified: 06/19/2012
Filesize: 265.06 kB

NURC-SP-2009-002. NATO Undersea Research Centre Marine Mammal Risk Mitigation Rules and Procedures. Kendra L. Ryan. November 2009.

A Matlab package for mission generation of a fleet of gliders. Version 1.0 A Matlab package for mission generation of a fleet of gliders. Version 1.0

Date added: 11/01/2011
Date modified: 09/06/2012
Filesize: Unknown

A Matlab package for mission generation of a fleet of gliders. Version 1.0. Alvarez, A.; Martinez, M. NURC-SP-2011-003. November 2011

The advent of new ocean observing technologies creates new scientific demands. Integrating oceanographic information and harmonizing different observational capabilities are among them. The latter concerns allocating and complementing observational resources to maximize the information content of the oceanographic data collected by a network of different oceanographic platforms. Compatibility between the observing capabilities of the different nodes must be found designing optimum sampling strategies to allow an accurate representation of oceanographic process. These sampling strategies could adapt to the evolution of the environment, considering possible motion limitations of part of the platforms in the network. This report describes a software developed to increase the optimality of sampling missions of a network of underwater gliders that may differ in control and motion capabilities. The procedure employs a statistical model of the spatial variability in the area of interest, extracted from a numerical model. The statistical model is then employed to compute sampling designs minimizing the spatial average variance of the estimated field with respect to the sampled locations. Simulated Annealing is employed as the minimization procedure to find the optimal trajectories of the autonomous platforms. The resulting optimum sampling design incorporates existing operational constraints and platform limitations

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