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Data Fusion and Reasoning Frameworks for Space Situational Awareness


Space Vehicles Directorate, RV/Space and Planetary Sciences

RO# Location
13.40.01.B8307 Kirtland Air Force Base, NM 871175776


Name E-mail Phone
Lovell, Thomas Alan 505.853.4132


The Air Force has a pressing need to be able to detect and characterize unexpected space object behavior and to better understand the potential for conjunctions among the many objects that are in geosynchronous orbit. Unfortunately, the detection of abnormal variations in the orbits of objects is imbued with several different kinds of uncertainties that need to be quantified with new methods. Areas of relevant research include:

(1) Modeling of Data Fusion. Multi-sensor data fusion is the process of combining or integrating measured data originating from different active or passive sensors to produce a more specific, unified dataset or system model about an entity (or object), or event of interest, that has been observed by a human in order to make decisions about object avoidance or object monitoring. A fusion process is meant to reduce the uncertainty of predicting the state of, or identifying, the observed object. Among the many models of data fusion, including statistical inference, Bayesian inference, Dempster-Shafer evidence theory, artificial neural networks, and Boolean voting algorithms, there is a need to understand and use fuzzy logic fusion approaches because these are capable of fusing linguistic information from human observations along with multi-sensor data.

(2) Situational Awareness. In determining a situation between two objects in space (e.g., conjunction, normal maneuvers, nuisance encounters), fusion of information from various databases is required. The information will have different degrees of uncertainty. When the level of uncertainties increases, situational awareness requires exact reasoning with increasing levels of data fusion. Using only numerical procedures, it is very difficult to model uncertainties to enable an accurate assessment of a space event (e.g., conjunction) because sometimes there is more than one decision for the same situation. Methods such as fuzzy set theory and evidence theory need to be explored in cases where some of the information about space situations is linguistic or conflicting.

(3) Common Reasoning Framework. Satellite control requires significant reasoning frameworks to address the myriad uncertainties associated with potential conjunctions and other space-borne maneuvers. A computational platform is being developed by AFRL personnel that requires development efforts in building a computational common reasoning framework, which integrates data from various orbital and intelligence sources and attempts to conduct space situational awareness for purposes of decision making on the part of operational analysts. This framework addresses different forms of uncertainties with different analytical approaches that must be integrated into a useful decision support system.



Subramaniam, Burks, Dixon: Transactions of the ASABE 52(5): 1411-1422, 2009

Noureldin, El-Shafie, Taha: Engineering Applications of Artificial Intelligence 20: 49-61, 2007

Raol: Multi-Sensor Data Fusion with MATLAB. CRC Press, 2010


Space situational awareness; Geosynchronous orbits; Conjunctions; Decision making; Data fusion; Uncertainties;


Citizenship:  Open to U.S. citizens
Level:  Open to Postdoctoral and Senior applicants
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