Association of Critical, Infrequent Data

The objective of this Phase I project is to demonstrate the feasibility for enhancing fusion target tracking with data from multiple, distributed sensors by improving the processing of measurement that will be called special data. Special data is measurement data that is critical (vitally useful information) but occur infrequently, such as provided by some sensors in air targets. For example, they may include only 1-D or 2-D position information, such as, angle only data or one angle plus range data. If the special data is kinematic information, it might be measurements from an IR sensor (with a large field of regard) that provides infrequent but very accurate angle only position (2-D) data. The time between arrivals of special data from a specific sensor might be fractions of an hour rather than a typical sensor that is factions of a minute. Thus, traditional processing would lead to misassociations of special data that would substantially degrade performance of fusion tracks, processed features. Even advanced fusion processors that employ current multiple-frame data association methods, such as multiple hypothesis trackers, might be too computation intensive if adjusted to accommodate the interval between the occurrences of the special data.