SBIR-STTR Award

At the core of the challenge facing MDA today is one of adversary air and missile threats being able to fly non-ballistic, highly maneuvering hypersonic trajectories all while having the capability of close formation flight of multiple missile threats. This problem results in challenges related to detection of the threat targets as well as the resulting filtering and data association problem. There is a tight coupling between the filtering and data association aspect of the problem. At its core, associating new sensor measurements to existing tracks requires (1) finding all possible associations, (2) scoring these possible associations, and (3) selecting the best associations. The number of possible associations can be reduced by both accurate prediction of the target state to the current time of the sensor measurements or use of signature measurements from wide band radar or EO sensors for association. For this effort, we are proposing a two-pronged solution to address the advanced data association problem for emerging threats. The first is to learn target destination, trajectory, and maneuver tactics to augment physics-based models to predict target state for association to measurements; and the second is to learn possible association between signature measurements and existing tracks. Approved for Public Release | 20-MDA-10398 (2 Mar 20)

The Integrated Air and Missile Defense (IAMD) threat environment has become more complex and challenging due to advances in adversary technology. Trusted and accurate data association can be achieved by reducing the inherent prediction uncertainty and the resulting data association uncertainty can be reduced by recognizing that IAMD threats follow trajectories that are planned to accomplish intended missions. The targets may maneuver but they eventually must reach the goals of the mission. Using machine learning we can predict threat behaviors, routes, and destinations which can be used to reduce association uncertainty. We are proposing to develop a Trusted Intelligent Data Association software prototype that combines machine learning for behavior understanding with a physics-based destination aware Interacting Multiple Model (IMM) and a data association manager to perform trusted association compatible with the data association uncertainty. Approved for Public Release |21-MDA-10789 (21 Apr 21)