OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Emerging Threat Reduction 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: Delivery of software capability that accurately assesses and analyzes detected launches and assigns a probabilistic inference classifying said launch as a Direct Ascent Anti-Satellite (DA-ASAT) launch or not. Solution must be able to integrate with existing workflows through open API that can be leveraged with existing systems and meets development and operational environment requirements. DESCRIPTION: Direct Ascent Anti-Satellite (DA-ASAT) launches pose a significant risk because of the destructive potential they possess. A single DA-ASAT event can cripple ground-based capabilities and cause on-orbit debris that presents further risk to space systems operations. The ability quickly and accurately identify DA-ASAT launch events provides a particular strategic advantage allowing for an increased response time and reducing the element of surprise that would be prevalent in the event of a connecting DA-ASAT launch. While there are manual processes in place to identify and alert of DA-ASAT launches and associated likelihoods, they are labor intensive and there is an inherent risk of an event being overlooked given the magnitude of launch events and large number of variables. A normal space launch carrying satellites is not very different from a DA-ASAT launch except that DA-ASATs impact a target around the time they achieve orbit. Since the goal is impact another satellite, the velocities and trajectories of DA-ASATs will differ from peaceful launches. Leveraging data sources such as two-line elements (TLEs), state vectors, or other similar information available through satellite orbit catalogs, data science methods such as artificial intelligence, machine learning, advanced statistical modeling, or other methods can be used to produce insightful information. Space Systems Command (SSC) Space Domain Awareness (SDA) Tap Lab is seeking solutions to provide a software capability that can automatically assess and analyze launch events in real-time (or near-real time as possible) and classify launch events as DA-ASAT launch events while also identifying targeted satellites if applicable. There are four key deliverables that the solution must provide. 1) Probabilistic inference classifying the launch event as either a DA-ASAT launch or not If the event is determined to be a DA-ASAT launch, then the following deliverables are required: 2) List of targeted satellites with associated probability risk scores 3) Time to impact or time to explosion of DA-ASAT 4) Calculated anticipated miss distance The software product must be able to be integrated into existing toolchains that are used by USSF operators and the SDA Tap Lab. As this capability is only one specific functionality to be used in conjunction with other tools to provide a wholistic understanding of space domain awareness and space operations, it is pivotal that the solution be able to seamless integrate with existing systems through API call. The solution must also meet all development and operational requirements for system integration and use on classified systems. Potential data sources to leveraged for development of this topic can include but are not limited to: - Public catalog of orbits via space-track.org and European Space Agency - Commercially available orbits via Unified Data Library (UDL) - Publicly available orbits via amateurs and hobbiyists - Notice to Airmen (NOTAMs) - Multi-source and publicly available contextual information (news feed, blogs, social media) PHASE I: This topic is intended for technology proven ready to move directly into Phase II. Therefore, Phase I awards will not be made for this topic. The applicant is required to provide detail and documentation in the D2P2 proposal which demonstrates accomplishment of a Phase I-type effort, including a feasibility study. This includes determining, insofar as possible, the scientific and technical merit and feasibility of ideas appearing to have commercial potential. It must have validated the product-mission fit between the proposed solution and a potential Air Force and/or Space Force stakeholder. The applicant should have defined a clear, immediately actionable plan with the proposed solution and the DAF customer and end-user. The feasibility study should have: 1. Clearly identified the potential stakeholders of the adapted solution for solving the Air Force and/or Space Force need(s). 2. Described the pathway to integrating with DAF operations, to include how the applicant plans to accomplish core technology development, navigate applicable regulatory processes, and integrate with other relevant systems and/or processes. 3. Describe if and how the solution can be used by other DoD or Governmental customers. In addition to the feasibility study applicants should demonstrate a framework algorithm to use a proof of concept for classifying launch events as DA-ASAT launches and identifying training dataset that can be used in model development. PHASE II: Phase II of this project focuses on advancing the capability developed in Phase I to accurately assess and analyze launch events, with a specific emphasis on improving evaluation metrics such as accuracy, precision, and F1 score, and expanding the scope of launch events assessed. The goal is to deliver a software capability that can effectively identify Direct Ascent Anti-Satellite (DA-ASAT) launches in real-time or near-real-time and provide probabilistic inferences classifying these events while also identifying targeted satellites if applicable. Leveraging data sources such as two-line elements (TLEs) and state vectors, advanced data science methods like artificial intelligence and machine learning will be employed to enhance the analysis. The solution must deliver four key functionalities: probabilistic classification of launch events as DA-ASAT or not, identification of targeted satellites with associated probability risk scores, time to impact or explosion of DA-ASAT, and calculated anticipated miss distance. Additionally, seamless integration with existing workflows through an open API is crucial, ensuring compatibility with USSF operators and the Space Systems Command (SSC) Space Domain Awareness (SDA) Tap Lab toolchains. The solution must also meet all development and operational requirements for integration and use on classified systems. Phase II builds upon the Phase I feasibility study, demonstrating scientific and technical merit, validating product-mission fit, and defining an actionable plan for collaboration with Air Force and/or Space Force stakeholders. PHASE III DUAL USE APPLICATIONS: Phase III of the project aims to scale the finalized model developed in Phase II to encompass the analysis of all launch events, not limited to just DA-ASAT launches. This expansion broadens the scope of the solution to provide comprehensive launch event analysis, thereby enhancing space domain awareness and operational readiness. In addition to scaling the model, Phase III focuses on expanding notification functionality to ensure timely and actionable alerts regarding significant launch events. This could involve the development of advanced notification systems capable of disseminating critical information to relevant stakeholders in real-time or near-real-time, enabling prompt decision-making and response. Furthermore, Phase III may involve the integration of additional data sources and analytical techniques to enhance the accuracy and reliability of launch event analysis and notification capabilities. Collaboration with key stakeholders, including the Air Force, Space Force, and other governmental agencies, will be essential to ensure alignment with operational requirements and maximize the utility of the solution. Overall, Phase III represents a significant advancement in the development and deployment of the launch event analysis system, furthering its effectiveness and impact in enhancing space situational awareness and mission success. REFERENCES: 1. P. Saunders, C. Lutes, "China's ASAT Test: Motivations and Implications", National Defence Univ Washington DC Institute for National Strategic Studies, vol. 46, 2007.; 2. A. Kawthalkar, M. Shah, I. Prachchhak, "Modeling and Simulation of a direct-ascent anti-satellite missile using Kerbal Space Program (KSP)", Aerospace Systems, vol. 5, May 2022.; KEYWORDS: Direct Ascent Anti-Satellite (DA-ASAT); launch classification; target identification; threat detection; AI-ML
