Space Signatures for Rapid Unambiguous Identification of Satellites

Award Information
Agency:
Department of Defense
Branch
Defense Advanced Research Projects Agency
Amount:
$99,972.00
Award Year:
2013
Program:
SBIR
Phase:
Phase I
Contract:
FA9453-13-M-0020
Award Id:
n/a
Agency Tracking Number:
D122-010-0129
Solicitation Year:
2012
Solicitation Topic Code:
SB122-010
Solicitation Number:
2012.2
Small Business Information
10440 Little Patuxent Parkway, Suite 600, Columbia, MD, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
172216827
Principal Investigator:
JacobGriesbach
Aerospace Engineer
(719) 482-8911
jgriesbach@applieddefense.com
Business Contact:
TomKubancik
VP, Advanced Programs
(410) 715-0005
tkubancik@applieddefense.com
Research Institute:
n/a
Abstract
Many of today's sensors collect various data types beyond the traditional radiometric (range) or photometric (angles) that we call Space Object Identification (SOI) data. These data sources can yield discriminating satellite features and present a clear opportunity for correlation techniques to provide POI and improved track custody. We can use light reflectivity magnitude profiles and inverse synthetic aperture radar imaging to model spacecraft attitude. Heat signature profiling may be established with IR sensing as objects ascend and descend to/from Earth eclipsing. Maneuver models and profiling may be obtained as objects station-keep and perform momentum dumps. Multi-color and/or hyperspectral photometry may be used to infer materials of the satellite's composition. RF transmissions may be analyzed spectrally to characterize what frequencies and coding techniques are used. We propose a new approach to data correlation. Our Phase I effort will research and design a prototype Bayesian discrimination framework to object identification and recognition. As an initial form of representative SOI data, we will develop an application to generate predictive optical magnitude (light curve) data, representative of actual observational data. We will modify a Multiple Model Adaptive Estimator (MMAE) approach to show how our core Bayesian discriminator concepts can efficiently and rapidly improve positive identification of catalogued (modeled) and un-catalogued (un-modeled) space objects. In addition, we will develop a tree-based taxonomy of representative 3D models to represent a variety of alternatives for the Bayesian discriminator. Finally, we will investigate the availability and accessibility of SOI data sources for future incorporation to the Bayesian discriminator for a possible Phase II follow-on effort.

* information listed above is at the time of submission.

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