The number and variety of missile-based threats places extraordinary stress on the sensor networks and resources used in U.S. missile detection and defense.  Sensor networks themselves are extensive, and, in a defense situation, even basic tasks like target search and detection, target acquisition, target discrimination, and target tracking can engage a variety of sensor resources (e.g., electro-optical and infra-red (EO/IR) sensors combined with multi-mode radar) that are based on a variety of platforms and locations (e.g., sea-based, aerial (i.e., UAVs), and space-based).  Detection and defense of a dynamic, multi-threat attack generates competing demands on these sensor resources.  Given the variety of threats and the complexity of the networks and resources involved, the efficient and effective allocation of sensor resources in real time is an important need.

In this initiative, KBSI is investigating innovative methodologies and algorithms for Efficient Sensor Management for Optimal Multi-Task Performance(ES-MaTe). The goal is to research and develop an efficient sensor resource management (SRM) method that will allow MDA planners to schedule sensor resources specific to missile defense networks.

Managing heterogeneous tasks like those characterize missile defense sensor networks involves a number of task-specific resource utilization constraints.  A key challenge in managing these types of tasks is that both the environment and the performance requirements dynamically change in the course of a defense situation.  For example, the relative importance of different tasks will change, along with accuracy and fidelity requirements,according to the phase of the target trajectory and the available information on target classification.  The nature of the scheduling problem can also change based on the changing environment—as a result, for example, of the shifting availability of sensor resources or when new targets are discovered.

The ES-MaTe initiative is investigating methods and algorithms for sensor resource management across multiple heterogeneous tasks and multi-sensor scheduling for competing tasks.  In addition, the ES-MaTe solution will provide task specific constraints satisfaction on sensor resource utilization as well as performance constraints to ensure minimum prescribed performance for each sensor related task.  The longer term goals of the initiative are to develop a comprehensive sensor management solution and migrate this technology to various other environments involved in resource allocation scheduling.