Tactical ELINT Analyzer

Synthetic aperture radar (SAR) images, while important in battlefield analysis estimates, are difficult to interpret. Military assets typically appear as dots on an image but so do rocks, fence posts, buildings, and other non-military ‘clutter.’ By considering various patterns of dots, the U.S. Army’s Image Exploitation System (IES) seeks to determine which of these patterns represent military forces. Furthermore, once such a determination has been made, the system attempts to ascertain higher level attributes such as force type. Battlefield intelligence includes more than just SAR images, of course, and this additional intelligence can augment and/or refine the overall estimate of the battlefield. ELINT is one example. CCR has developed a component of the IES, called Tactical Elint Analyzer (TEA), to support force estimation based on ELINT evidence

The modern battlefield is populated by large numbers of non-communications emitters, most commonly radars. These electronic emitters support a variety of military functions from air defense to meteorology. However, a single emitter is usually tied closely to a specific military function (e.g., early warning for air defense). Friendly assets scan the electromagnetic spectrum ‘listening’ for these non-communications emitters and report information for each intercept as part of a Tactical Electronic Intelligence (TacElint) message. TacElint messages typically consist of an estimate of the emitter type, an elliptical error probable for emitter location, and various electronic attributes, such as frequency, pulse duration or width, and pulse repetition frequency. TEA combines evidence from TacElint messages with terrain and doctrinal information to infer the locations and types of forces present in an area of interest.

TEA responds to an IES request for information about a specific SAR image by first retrieving relevant ELINT intercepts from an internal database based on location and time. The intercepts are then self correlated to produce a refined estimate of the number and position of the various battlefield emitters. TEA uses a terrain based prior probability to further refine emitter locations. Finally, emitter location estimates are combined with emitter to force models to predict the disposition of higher level forces in the vicinity.