Into the Wild-Transitioning Basic Rsh Algs to Ops

OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Space Technology The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. OBJECTIVE: Transition basic research algorithms developed previously with AFOSR 6.1 dollars into operations. These algorithms were developed to improve space domain awareness (SDA) to monitor the satellites in earth orbits. AFOSR is working with the Space Systems Command (SSC) TAP lab and with the Space Operations Command (SpOC) to transition and test these algorithms. DESCRIPTION: AFOSR under collaboration with USSF Delta Operators identified several research algorithms that have the potential to improve space domain awareness (SDA) for USSF operational community. These algorithms were developed by academic researchers at many universities, tested on various platforms using many different data sources. These algorithms are in the area of orbit prediction and uncertainty quantification, change detection, characterization, tracking, and tasking. This effort will take those algorithms and mature them to perform on a single platform and test/evaluate the algorithms on relevant operational data. Algorithms that show promise to improve SDA for USSF will be matured further for potential transition to operations. A support letter from USSF is available upon request from AFWERX. Reference papers for some of the subject algorithms are shown in the reference cell. AFOSR is working collaboratively with SSC and SpOC on this effort. We have transition paths already identified. AFOSR only has 6.1 funds and cannot mature these algorithms. The SBIR program is perfect to provide the maturation of these algorithms for transition to operations. PHASE I: Phase I will test the previously developed algorithms on operational data in collaboration with the SSC TAP lab. Testing results will be used to identify algorithms maturity and individual algorithm transition plan. Testing will also be used to mature and improve algorithm performance. A common platform where the algorithms will be integrated into in phase I will be identified, such as AFSIM, and a plan developed for how the algorithms will be integrated into the platform and how it will pull data. A common data format will be identified. Phase I output will be testing data, identification of a common platform to access data and run the algorithm, and a plan for algorithm integration. PHASE II: Phase II will be a software effort to put all subject algorithms onto a single platform (such as AFSIM), tested with relevant data provided by USSF, conduct wargaming scenarios to evaluate the algorithms, identify strengths and weaknesses of subject algorithms. The results of Phase II will be used to identify which algorithms should be transitioned to operational support for USSF. PHASE III DUAL USE APPLICATIONS: Phase III will be an operational hardened software package with these new SDA algorithms that will be transitioned to SpOC and SSC for space operations. REFERENCES: "Hao Peng and Xiaoli Bai, Imrpoving Orbit Prediction Accuracy through Supervised Machine Learning, Advances in Space Research (2018); S. Fedeler, M. J. Holzinger, W. Whittacker, Tasking and Estimation for Minimum-Time Space Object Search and Recovery, Journal of the Astronautical Sciences, Vol. 69, pp. 1216-1249, July 2022; S.N. Paul, B. Little, C. Frueh, Detection of Unknown Space Objects Based on Optimal Sensor Tasking and Hypothesis Surfaces Using Variational Equations, Journal of Astronautical Sciences, 10.1007/s40295-022-00333-z, July 2022; Balducci, M., and B.A. Jones, Probability of Collision Estimation and Optimization Under Uncertainty Utilizing Separated Representations , Journal of the Astronautical Sciences, Vol. 67, No. 4, pp. 1648-1677, 2020."; KEYWORDS: space domain awareness; space domain awareness algorithms; data to decision; orbit determination; satellite tracking; satellite characterization