Contextual Reasoning for Threat Classification Refinement

Understanding and classifying inbound objects quickly provides critical information necessary to perform optimal midcourse defense through precision assignment and targeting of the highest priority objects. To enhance the accuracy of this classification, we pursue advances in contextual reasoning. Recognizing that the threat may not appear as was predicted, that which can be discerned from the context about the engagement and threat presentation could be key. We propose research to develop a better classifier that uses disparate sensor data to assess classification of objects, including over time (as opposed to single shot classifiers) so that salvo firing control can be better informed to target high priority objects. The classifier will be mission assured by informing the operator of an unknown classification of an object rather than best guess by way of predefined metrics in order to prevent misclassifications on zero-day events. Approved for Public Release | 21-MDA-11013 (19 Nov 21)