Realistic State and Measurement Error Uncertainty Computation and Propagation for Space Surveillance and Reconnaissance

Award Information
Agency:
Department of Defense
Branch
Air Force
Amount:
$99,959.00
Award Year:
2010
Program:
STTR
Phase:
Phase I
Contract:
FA9550-10-C-0077
Award Id:
94961
Agency Tracking Number:
F09B-T11-0145
Solicitation Year:
n/a
Solicitation Topic Code:
AF 09TT11
Solicitation Number:
n/a
Small Business Information
1300 N. Holopono St, Suite 116, Kihei, HI, 96753
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
784201746
Principal Investigator:
DaronNishimoto
Project Manager
(808) 268-2733
daron.nishimoto@pacificds.com
Business Contact:
DonForrester
CFO
(808) 268-4478
don.forrester@pacificds.com
Research Institute:
CUBRC, Inc
Adam Stotz
4455 Genesee Street
Buffalo, NY, 14225
(716) 204-5137

Abstract
The accurate estimation of real space object (RSO) motion is subject to the complex interaction of gravitational forces, non-conservative drag terms and variable solar radiation pressure. Currently many operational algorithms rely on quasi-linear models with Gaussian-Input-Gaussian Output (GIGO) assumptions that do not capture the non-Gaussian nature of RSO error characteristics. An innovative approach based on a combination of the Gaussian Sum Filter (GSF) and Generalized Multiple Model Adaptive Estimation (GMMAE) scheme is proposed to fully describe the probability density function (pdf) associated with RSO tracks. The GSF can provide an accurate pdf construction of the state error process by approximating the Fokker-Planck-Kolmogorov equation in a computationally efficient manner. The GMMAE scheme generalizes the MMAE process by incorporating multiple time-steps of the residual sequence back into the estimation process and using the likelihood of the residual sequence in order to provide a weighted average of the assumed parameter elements in the unknown covariance matrices. The novel mathematical framework provided by the GSF and GMMAE will be implemented against a realistic RSO use case with the goal of assessing the feasibility of using these more realistic recovered state and covariance estimates within the current space surveillance network (SSN) environment. BENEFIT: Currently, the SSN uses the NORAD SGP4 orbit models for predicting satellite positions that do not have the associated covariance estimates. PDS will provide a performance assessment of utilizing these innovative orbit estimation and RSO track association algorithms developed under this project by testing their accuracy and responsiveness of RSO tracking against realistic use cases generated with an innovative space surveillance network (SSN) simulator. Once these algorithms are validated under "real world" simulations, PDS will test and validate these algorithms with actual SSN data. PDS intends to work closely with the Air Force in transferring technology for their critical objectives. The primary DoD end-customer for these algorithms is the JFCC-Space through the Joint Space Operations Center (JSpOC), which detects, tracks, and identifies all man-made objects in Earth orbit. Through current program experiences, PDS understands the acquisition process involved in transitioning algorithms from concept to validation, development, testing, (SMC SSA Technology Branch) and deliverance of an operational product to the warfighter (AF Space Command).

* information listed above is at the time of submission.

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