AI (Artificial Intelligence)-based algorithms have great potential for inter-operation of coalition ISR (intelligence, surveillance, and reconnaissance) systems, but rely on realistic data for training and validation. Getting such data for coalition scenarios is hampered by military regulations and is a significant hurdle in conducting basic research. We discuss an approach whereby training data can be obtained by means of scenario-driven simulations, which result in traces for network devices, ISR sensors and other infrastructure components. This generated data can be used for both training and comparison of different AI based algorithms. Coupling the synthetic data generator with a data curation system further increases its applicability.
Dinesh Verma, Greg Cirincione, Tien Pham, and Bong Jun Ko, "Generation and management of training data for AI-based algorithms targeted at coalition operations," Proc. SPIE 10635, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX, 106350U (Presented at SPIE Defense + Security: April 18, 2018; Published: 4 May 2018); https://doi.org/10.1117/12.2305244.
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