KAB Laboratories focuses its Multi-INT Fusion research to develop fusion services across distributed networks that can provide tracks, trends, behaviors, and highlight…
KAB Laboratories focuses its Multi-INT Fusion research to develop fusion services across distributed networks that can provide tracks, trends, behaviors, and highlight anomalies. KAB uses a mix of theory and best practices in modern data trends that affords fusion of high volumes of data. These include: ontology mapping, Bayesian forecasting, Dempster-Shafer theory, multiple hypothesis theory, n-gram extraction, probabilistic latent semantic analysis (PLSA), and frequency histograms.
Specifically, KAB develops:
- Algorithms that fuse unstructured and structured data to develop Objects (Level 1 Fusion), identify situations (Level 2 Fusion), and threats (Level 3 Fusion);
- Algorithms that overlay geopositional multi-INT reports directly into Full Motion Video;
- Analytic services for near real-time and historical event comparison; and
- Analytic services including alerting, change detection, and threat identification that enable users to identify and analyze networks.