Distributed Satellite Resource Management for Defensive Counterspace

The DF&NN team will integrate Space Situational Awareness (SSA) & Defensive Space Control (DSC) R&D based upon the DF&NN Dual Node Network DF&RM technical architecture. The DF&NN subcontractor Modern Technology Solutions Inc (MTSI) brings extensive operations experience to define relevant needs. We will focus on six problem areas: 1. Space Environment SSA/DSC: How to fuse space weather, BFS/SAS, and historical Smoking Gun results to re-plan resources. .   2. Satellite Proximity Threat SSA/DSC: How to fuse proximity detection sources to include an on-board satellite capability to detect abnormalities that enable an immediate response.  3. Direct Ascent SSA/DSC: How to manage resources to ascertain DA threats.  4. Laser/RF Threat SSA/DSC: How to fuse on-board data for threat characterization and response. 5. Performance Assessment and Process Management: How to associate SSA/DSC outputs to estimated truth and use this to automatically determine the abnormality detection thresholds, event tracking and relationship parameters, and response tasking to improve performance.  6. SSA Context Assessment (CA) and Concurrency Management (CM): How to recognize external context information that is relevant to the current SSA picture and red/blue courses of action (COA). Then how to estimate the concurrency of this relevant context. Then what to do about significant concurrency differences.   BENEFITS: DF&NN is currently approaching the end of its first SBIR Phase 2 during which time it has delivered the satellite-as-a-sensor (SAS) abnormality detection software that is the core software to be used in the $30M Blue Force Status (BFS) program. DF&NN started on funding from BFS in October, 2008. Satellite-as-a-sensor (SAS) was also used to drive JSARS for AFRL.  DF&NN played a major role in the JSARS program and the evidence of the JSARS commercial potential has been demonstrated by of the funding provided in 2008 by the ESC JFCC Space Integrated prototype (JSIP) and the SMC ISSA Space Threat Awareness Characterization System (STACS) on which DF&NN has provided fusion web services.   The competitive advantage of this DF&RM technology is in its affordability derived from the reusability provided by the DNN technical architecture and its extendibility derived from the data driven pattern learning software, as described in the technical portion of this proposal.  Due to the substantial data driven nature of the DF&NN team software, the products from this SBIR Phase II will be easily extendable to detect, recognize, and track abnormalities in any commercial or government system that provides state of health data.  Homeland security and commercial system operations have interest in computer security abnormality detection, recognition, and tracking.  In fact, the operators of any system that outputs state of health (SOH) data should be interested in these affordable capabilities for fault isolation and response especially since they are data-driven and can be easily modified for new SOH data.  Commercial satellite operators should be interested in having these products applied to their satellites.  In addition, the situation assessment and response products provided in this Phase 2 SBIR effort will be directly extendable to these commercial and government applications.