Data Fusion for Space Situational Awareness

Scientists demonstrate data fusion and decision support tools for satellite threat analysts.

Satellites greatly enhance US defense operations; however, these key assets are vulnerable to perils such as space weather and acts of aggression. Unfortunately, it is difficult to determine the cause of anomalies from the ground. What may first appear as a routine system glitch may, in fact, be something much more serious. Providing the ability to detect—and in some cases, predict—events via multiple data sources can be critical to mission success and the safeguarding of space assets. Threat analysts must be able both to distinguish external, man-made threats, natural threats, and environmental conditions from internal, satellite bus anomalies in real time and to subsequently perform mitigating actions.

To address the challenges that analysts face, AFRL researchers are using a multidisciplinary approach— one that incorporates important human systems integration (HSI) considerations into the design of space data fusion and display methods. The merger of these key research areas will enable the team to devise a system that assists human operators in detecting subtleties that may otherwise go unnoticed. In April 2006, researchers demonstrated this capability at the Center for Research Support, Schriever Air Force Base (AFB), Colorado. Five small business contractors worked together under individual Small Business Innovation Research contracts to perform the associated research. This work directly supports the Space and Missile Systems Center Space Superiority Systems Wing, as well as various activities occurring within the Defense Meteorological Satellite Program’s, Technology Applications Division in support of environmental space situational awareness.

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The technology demonstrated thus far involves an integrated suite of multilevel data fusion capabilities, including (1) abnormality detection and classification, (2) event tracking, (3) event relationship/situation assessment, and (4) preliminary individual visualization displays for both the operator and the analyst. The system fuses data from a variety of sources, such as satellite telemetry, space weather, orbital proximity, and radio frequency input, via the Air Force Satellite Control Network Link Processing System. To enhance its event detection algorithm effectiveness, the system converts telemetry data to “Satellite as a Sensor” abnormality reporting data via a machine learning and signature clustering technique based upon historical datasets.

Many researchers consider data fusion a powerful technology for satellite threat detection. Event detection algorithms assess a situation according to different kinds of data by employing a network of fusion nodes that “divide and conquer” the problem. In this approach, algorithms at each fusion level have finite responsibilities, but collectively, they accommodate many different data sources having varying levels of detail and certainty. AFRL is developing a modular threat analysis architecture that will facilitate the following:

 

• Autonomous detection of threats or other abnormalities
• Abnormality tracking
• Tracking of associated heterogeneously related events
• Assessment of mission impact
• Autonomous situational response recommendations and response effects assessments against commander’s intent