| ||The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), which controls the export and import of defense-related material and services. Offerors must disclose any proposed use of foreign nationals, their country of origin, and what tasks each would accomplish in the statement of work in accordance with section 3.5.b.(7) of the solicitation.|| Objective: ||To develop and demonstrate automated resource management (RM) technologies that perform management of real-time defensive counterspace (DCS) and space situational awareness (SSA) activities.
|| Description: ||In recent years, there has been a focus towards applying data fusion technologies for the detection and discrimination of spacecraft threats. The complement of the Joint Directors of Laboratories (JDL) fusion model is the RM model that allows for formal management of each of the JDL fusion levels. Based upon a-priori information, the RM can provide automated tasking, prioritized response options, or decision aids for user response tasking. These response options need to be more sophisticated than simple checklist-based courses of action in service today, because multiple simultaneous events may necessitate non-linear combinations of time-phased responses. Automated response options provided to operators from RM can be demonstrated to be more suitable than expected from low-experience military or civilian personnel operating only from checklists. In addition, automated RM should significantly reduce the time needed to appropriately respond during periods of space warfare. RM will advise satellite operators of possible response actions in a timely manner to the situation as provided by Level 0-3 fusion developed for DCS and SSA. These include: abnormality detection/recognition, abnormal event tracking, event relationship and situation tracking, and mission impact prediction. RM will be driven by the specific individual satellite mission objectives (i.e., Level 3 management outputs). These will be used to drive Level 2 resource relationship (e.g., resource conflicts and synergisms) management, Level 1 independent resource scheduling, and Level 0 signal management. This proposal seeks to develop prototype Levels 0, 1 and 2 RM algorithms for satellite defense, then demonstrate the RM approach is general enough to be successfully applied to other missions. The resources considered in this effort are satellite defensive commanding (e.g., notifying the appropriate command, closing shutters, changing subsystem operating modes), maneuvering the satellite, modifying the communications, processing management, and sensor and data collection management. As an example, distributed Level 1 RM can generate relevant cross-unit recommendations. Distributed Level 1 RM at a SOPS could generate RM recommendations that include increased data fusion vigilance for similar problems on other satellites or increased alerting for changes in space environment conditions once environmental impacts materialize. To improve the operator situation awareness and response decision making the proposer should provide for sensor, communications, and/or data collection management, visualization, and reporting.
|| ||PHASE I: The focus of phase I is to develop and demonstrate Level 0 and 1 RM prototypes specifically tailored to an operational unit, such as a satellite operations squadron or center that has direct control over organic resources.
|| || ||PHASE II: During phase II the Level 0 and 1 RM algorithms developed in phase I will be refined for more sophisticated threat conditions that could occur in multi-satellite constellations or cross-constellation/cross-network RM situations. The RM capability will be integrated with Level 0-3 abnormality event fusion using SAS, electromagnetic environment measuring system (EEMS), and space weather data.
|| ||DUAL USE COMMERCIALIZATION: Military application: Highly applicable to threat and abnormality detection. Applicable to Air Force needs. Commercial application: Data Fusion & Resource Management tools are applicable to the management of any process requiring the fusing of multiple heterogeneous information sources in order to improve mission execution.
|| References: ||1. Steinberg, A. and C. Bowman, “Rethinking the JDL Data Fusion Levels”, NSSDF JHAPL, June, 04.
2. Bowman, C. L., “The Dual Node Network (DNN) Data Fusion & Resource Management (DF&RM) Architecture” AIAA Intelligent Systems Conference, Chicago, September 20-22, 2004.
|Keywords: ||JDL fusion model, threat detection, data fusion|