RF-IR Data Fusion

deciBel Research, Inc. and the Electrical Engineering Department at the University of Mississippi - the deciBel Team - proposes to continue to develop and mature threat physics based classification, track association/track correlation, and dynamic attributes determination threat characterization algorithms that effectively and efficiently fuse data from multiple sensors. The classification algorithm will be based on matured version of the dBTASM the streaming single pulse classifiers dBTASM. The track association/correlation (dBATA/dBATC) and the dynamics attributes determination algorithm, dBTDAD, will be based on interfacing dBTASM with the Best Estimate of Track (BET) algorithm dBWager. These algorithms performance will be demonstrated within a new unique Cognitive Computing Decision Architecture (CCDA) anchored by a Cognitive Rational Intelligent Node (dBRaIN) processor, using illustrative Vignettes Scenarios. dBRaIN will be specifically designed to reason, make inferences, and decisions while dynamically sorting out, in real-time: a) information about, b) events related to, c) full characterization of, and d) learning from, all threats/threat events present during evolving data collection missions, in clear and degraded environments.