Developing confident fused estimates derived by combining multi-source data (e.g. sensor data, human intelligence reports and open-source data) is a challenge for intelligence analysts. Data that can provide evidence about terrorist network operations are sparse, uncertain, disparate, and can include both misinformation and disinformation. In particular, open source data is often uncorroborated and has incomplete or unknown pedigree. Intelligence analysts need improved information fusion capabilities that can 1) detect/recognize patterns that indicate operations of terrorist networks while 2) characterizing the uncertainty of pattern recognition consistent with the uncertainty of the evidence. This effort will develop a consistent confidence assessment capability based on analytical methods that are independent of the cognitive assessments of human intelligence analysts. Our Phase I effort will lead to improved models of terrorist networks that characterize uncertainty, enabling improved fusion of uncertain information for counter-terrorist operations. ALPHATECH proposes an innovative approach in developing a confidence assessment capability for information fusion of evidence on terrorist operations by applying the theory of complex networks to model the uncertainty in information sources and pedigree. To facilitate this effort, we will leverage an existing ALPHATECH prototype system for fusing relational evidence to establish link detection for counter-terrorist operations.
