Information Fusion and Situation Awareness using ARTMAP and Partially Observable Markov Decision Processes

Nathan Brannon; Gregory Conrad; Timothy Draelos; John E. Seiffertt; Donald C. Wunsch

doi:10.1109/IJCNN.2006.246950

Information Fusion and Situation Awareness using ARTMAP and Partially Observable Markov Decision Processes

Nathan Brannon
, Gregory Conrad
, Timothy Draelos
, John E. Seiffertt
, Donald C. Wunsch

Missouri University of Science and Technology

Research output: Contribution to journal › Article › peer-review

Abstract

For applications such as force protection, an effective decision maker needs to maintain an unambiguous grasp of the environment. Opportunities exist to leverage computational mechanisms for the adaptive fusion of diverse information sources. The current research involves the use of neural networks and Markov chains to process information from sources including sensors, weather data, and law enforcement. Furthermore, the system operator's input is used as a point of reference for the machine learning algorithms. More detailed features of the approach are provided along with an example scenario.

Original language	American English
Journal	Proceedings of the IEEE International Conference on Neural Networks (2006, Vancouver, BC, Canada)
DOIs	https://doi.org/10.1109/IJCNN.2006.246950
State	Published - Jul 21 2006

Disciplines

Electrical and Computer Engineering

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10.1109/IJCNN.2006.246950License: Unspecified

https://doi.org/10.1109/IJCNN.2006.246950

Cite this

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title = "Information Fusion and Situation Awareness using ARTMAP and Partially Observable Markov Decision Processes",

abstract = " For applications such as force protection, an effective decision maker needs to maintain an unambiguous grasp of the environment. Opportunities exist to leverage computational mechanisms for the adaptive fusion of diverse information sources. The current research involves the use of neural networks and Markov chains to process information from sources including sensors, weather data, and law enforcement. Furthermore, the system operator's input is used as a point of reference for the machine learning algorithms. More detailed features of the approach are provided along with an example scenario.",

author = "Nathan Brannon and Gregory Conrad and Timothy Draelos and Seiffertt, \{John E.\} and Wunsch, \{Donald C.\}",

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AU - Wunsch, Donald C.

PY - 2006/7/21

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AB - For applications such as force protection, an effective decision maker needs to maintain an unambiguous grasp of the environment. Opportunities exist to leverage computational mechanisms for the adaptive fusion of diverse information sources. The current research involves the use of neural networks and Markov chains to process information from sources including sensors, weather data, and law enforcement. Furthermore, the system operator's input is used as a point of reference for the machine learning algorithms. More detailed features of the approach are provided along with an example scenario.

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DO - 10.1109/IJCNN.2006.246950

M3 - Article

JO - Proceedings of the IEEE International Conference on Neural Networks (2006, Vancouver, BC, Canada)

JF - Proceedings of the IEEE International Conference on Neural Networks (2006, Vancouver, BC, Canada)

ER -

Information Fusion and Situation Awareness using ARTMAP and Partially Observable Markov Decision Processes

Abstract

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