Information Fusion and Situation Awareness Using ARTMAP and Partially Observable Markov Decision Processes
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.
N. Brannon et al., "Information Fusion and Situation Awareness Using ARTMAP and Partially Observable Markov Decision Processes," IEEE International Conference on Neural Networks - Conference Proceedings, pp. 2023-2030, Institute of Electrical and Electronics Engineers (IEEE), Jan 2006.
The definitive version is available at http://dx.doi.org/10.1109/IJCNN.2006.246950
International Joint Conference on Neural Networks 2006, IJCNN '06 (2006: Jul. 16-21, Vancouver, British Columbia, Canada)
Electrical and Computer Engineering
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