Fusion of Space Weather Data with Satellite Telemetry

Award Information
Agency:
Department of Defense
Branch
n/a
Amount:
$149,998.00
Award Year:
2012
Program:
SBIR
Phase:
Phase I
Contract:
FA9453-12-M-0036
Award Id:
n/a
Agency Tracking Number:
F112-066-1373
Solicitation Year:
2011
Solicitation Topic Code:
AF112-066
Solicitation Number:
2011.2
Small Business Information
1643 Hemlock Wy, Broomfield, CO, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
130770055
Principal Investigator:
Christopher Bowman
Owner
(303) 469-9828
cbowman@df-nn.com
Business Contact:
Christopher Bowman
Owner
(303) 469-9828
cbowman@df-nn.com
Research Institution:
Stub




Abstract
ABSTRACT: To support emerging requirements for continuous and automated Space Situational Awareness (SSA), there must be an understanding of the root causes of anomalies, as this directly affects courses of action. To further SSA, one must accurately attribute anomalies to environment versus man-made with a high confidence level. DF & NN will extend its Bayesian Fusion Node (BFN) software to fuse over 100 combined years of Enterprise-Satellite-as-a-Sensor (E-SAS) abnormality detections of SOH and space weather data including protons, electrons, and x-ray measurements for GOES at GEO and DMSP at LEO, plus Signal to Noise Ratio (SNR) data for GPS at MEO. The latter will be used to search for scintillation cloud attribution. Given our prior fusion tool development and unexpected abnormality detection success with these sources we are well-positioned to quickly demonstrate the prototype fusion of E-SAS abnormality detections and space weather tracks to assess if the weather is more correlated to satellite event tracks than man-made causes in all 3 space domains. DF & NN will perform an analysis of alternative approaches and assess the probability of detection, false alarm, characterization accuracy, timeliness, and effort required to adapt the approach to other satellites and space weather sources. BENEFIT: The competitive advantage of this DF & RM ANOM technology is in its affordability derived from the data-driven pattern learning software, ability to detect the unexpected abnormal signatures, and its extendibility/reusability derived from the DNN DF & RM technical architecture. The data-driven core of the DF & NN ANOM software enables it to be easily applied to detect, recognize, and track abnormalities in any commercial or government system. Operational prototypes of this capability are already operating on-line at to 2 satellite operations (SOPS) sites and these ANOM tools have been applied off-line to over 100 different large to enormous real data sets for over 190 combined years of data. DF & NN has a pending patent on the ANOM technology and plans to commercialize to many DoD and commercial systems in collaboration with our Commercialization Pilot Program (CPP) team member Lockheed Martin Corporation (LMC) (e.g., for SOPS, Remotely Piloted Aircraft (RPA), and other LMC products). We also have a Strategic Alliance & Joint Development Agreement with OLEA Systems Frank Morese, Chief Executive Officer, for commercial applications to include buildings, sensor systems, and factories. So DF & NN has the government and commercial teammate agreements and patents in place now to commercialize this affordable and high performance data-driven ANOM technology.

* information listed above is at the time of submission.

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