Abstract : Maritime Domain Awareness (MDA) requires the ability to accurately identify, track, and understand the rationale of the behavior of vessels given the surrounding contextual environment. Reasoning about such behaviors can ultimately lead to a better understanding of whether the behaviors are normal or anomalous. In order to adequately improve the quality and overall confidence of the output of the reasoning process that leads to the conclusions regarding the nature of vessel behaviors, a system must be provided with sufficient, high quality, track data from which such conclusions could be made. The nature of maritime data, particularly in specific environments, and the fact that Intelligence, Surveillance and Reconnaissance (ISR) assets in future network-centric systems are likely to be heavily requested due to the complexity and number of non-traditional missions, necessitates that these resources be effectively utilized across the mission space in order to maximize overall collection and hence derive good quality tracks for actionable tasking of limited resources. This paper will describe the development of ISR allocation optimization algorithms for the maritime domain, leveraging modeling and simulation capabilities from the Interactive Scenario Builder (ISB) decision support tool. The ISB is a 3D tactical decision aid and mission planning system that provides insight into, and visualization of, the RF environment. The ISB is used in the Joint Information Operations Command and in Navy IO Centers (Hawaii, Georgia, Norfolk, San Diego, and Whidbey Island). The optimization algorithms being developed within the ISB take into consideration areas of interest such as high interest vessel movements, shipping lanes, fishing areas, military exercises, ports of particular interest, area of high piracy, cargo, or past shipping incidents.
[1]
Han Yu,et al.
Evolving Sensor Suites for Enemy Radar Detection
,
2003,
GECCO.
[2]
Tal Shima,et al.
Assigning cooperating UAVs to simultaneous tasks on consecutive targets using genetic algorithms
,
2009,
J. Oper. Res. Soc..
[3]
Riccardo Poli,et al.
Particle swarm optimization
,
1995,
Swarm Intelligence.
[4]
Harold W. Kuhn,et al.
The Hungarian method for the assignment problem
,
1955,
50 Years of Integer Programming.
[5]
Rainer Storn,et al.
Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces
,
1997,
J. Glob. Optim..