Adaptive and Intelligent Space (AIS)

Objective: The United States Space Force (USSF) is seeking innovative solutions to inform the future of autonomous, resilient, and intelligent space operations through the Futures Series: Adaptive and Intelligent Space (AIS) Challenge—a strategic initiative led by Task Force Futures in partnership with SpaceWERX. This topic invites proposals that investigate the technical and operational feasibility of emerging space concepts and dual-use technologies capable of supporting coordinated satellite operations across Low Earth Orbit (LEO), Geostationary Orbit (GEO), eXtended GEO (XGEO), and the cislunar environment. In Phase I, offerors should focus on early-stage research activities—including literature reviews, modeling and simulation, trade space analyses, and university or non-profit research collaborations—to lay the groundwork for prototype or proof of concept demonstrations to be developed in a potential future Phase II. This exploration phase is intended to reduce risk, validate feasibility, and refine mission alignment before committing to integrated technology demonstrations. Objectives include: 1. Explore novel concepts and architectures that support enhanced autonomy, survivability, and responsiveness in degraded or adversarial space environments. 2. Assess the feasibility of onboard edge intelligence, predictive threat analytics, and autonomous decision-making systems suitable for bandwidth-limited or contested conditions. 3. Analyze modular, scalable systems—including sensor payloads, computing elements, and spacecraft buses—that can adapt to evolving missions and orbital domains. 4. Develop preliminary Concepts of Operations (CONOPS) aligned with future USSF mission needs and evaluate potential integration paths with Space Force operational constructs. Proposals should clearly define their Phase I scope, research methods, and collaboration plan with a research institution. While transition and commercialization planning are not the primary focus of Phase I, proposers should briefly articulate how the work could inform a Phase II prototype or proof-of-concept demonstration and support longer-term mission relevance. Description: This topic seeks to explore emerging technical concepts that could enable a future generation of autonomous, resilient, and networked satellite systems for U.S. Space Force (USSF) missions. The Adaptive and Intelligent Space (AIS) Challenge addresses foundational gaps in how space-based assets are allocated, coordinated, and managed under contested and communication-degraded conditions. The Space Force requires novel capabilities that provides timely, accurate, and actionable information on all objects and activities within the Earth-Moon system, to ensure spaceflight safety, protect critical assets, and deter hostile actions. Next generation sensor networks and data processing capabilities must adapt to fainter, more agile threats. This will require acquisition of new sensor and processing systems. As Resident Space Objects (RSOs) have become smaller, more distant, and inhabit ever larger fractions of the volume, the warfighter requires new technical tools to satisfy the need to search for and find objects in larger volumes, to fainter limits, more quickly, with specified revisit rates. In broad terms, the capability and technology need seeks new sensors and/or algorithms to Find, Fix, and Track (F2T2EA). To achieve the above, collaboration with small businesses and academia is necessary to ensure advancement in: - High-Performance Computing (HPC): Significant HPC resources to process the raw sensor data, perform object detection, and generate accurate orbital tracks. - Data Fusion and Correlation: The ability to fuse data from multiple sensors (both existing and new) is essential to create a coherent and comprehensive picture of the space environment. - Machine Learning (ML) for Automation: ML algorithms to automate object detection, anomaly identification, and threat assessment, enabling analysts to focus on the most critical events. The objective of this Phase I effort is to investigate the scientific, operational, and engineering feasibility of novel technologies supporting space control and battle management, with the goal of improving autonomous behavior, real-time responsiveness, and adaptability in orbital environments including LEO, GEO, XGEO, and cislunar space. Offerors are encouraged to propose early-stage research concepts that address one or more of the following focus areas. All work should include collaboration with a qualified research institution, and clearly define the technical approach, scope of analysis, and expected outcomes of a successful Phase I study: 1. Edge Computing & Algorithms: Investigate edge processing architectures and AI/ML algorithms that can operate onboard spacecraft, enabling autonomous, low-latency decision-making in support of space domain awareness and battle management. This topic area should improve the ability to autonomously manage space activities, and improve the ability to autonomously fuse, process, and filter data from multiple sources and sensors. Desired capabilities may include: - Technical feasibility of onboard threat analytics - Architectures for resilient, secure, and radiation-tolerant edge computing - Methods for reducing kill-chain latency by at least 50% and operator workload by at least 25% - Potential test environments (digital or orbital) for future validation - Discrimination in contested environments including resilience to data poisoning, sensor degradation, obfuscation, high noise Desired technologies may include: - Predictive threat analytics and orchestrated response - Thermal management and radiation-hardened solutions for edge and next-gen processing technologies (e.g. neuromorphic) - Data security - Agile and assured use of high-partition data - High-partition data agility and balanced software/AI integration - Anomaly or maneuver detection AI/ML algorithms and SDA data processing with high throughput - Tip & Cue capability between SDA and satellite coordination functions - Memory-safe language applications for autonomy - Improved ‘do-no-harm’ software for Autonomous Rendezvous and Proximity Operations - Zero-trust for on-board computation methods - Data compression algorithms - Simulation of SDA data to improve training of robust maneuver detection models - Cislunar simulation and visualization tools 2. Sensors Payloads: Explore modular sensor payload concepts with integrated processing that support multi-modal detection, threat characterization, and real-time insight generation. Emphasis should be placed on adaptability across mission profiles and orbital regimes. This topic area should improve the ability of the USSF to monitor, characterize, and maintain custody of space objects for a comprehensive sight picture. Desired capabilities may consider: - Integrated sensor-computing packages for persistent tracking - Techniques for novel SDA collection and data fusion - Higher fidelity persistent space object custody - Attribute in-space actions across multiple collection spectra (including visible, IR, RF, LiDAR) - Solutions for software-defined or reconfigurable payloads - Techniques for improving satellite change detection and maneuvers Desired technologies may include: - Passive RF, LIDAR, radar, IR, hyperspectral sensors - HPC-S (high-performance computing at the sensor) - Space-based sensing concepts suitable for GEO, XGEO, and cislunar 3. Bus Design: Assess spacecraft bus concepts that are modular, autonomously managed, and designed for mission flexibility and lifecycle extension. This topic area should enable improved integration of the prior two topics for advanced sensing and computing capabilities. Desired capabilities include: - Hardware modularity to allow rapid integration of emerging technology - Software modularity to allow integration of autonomous capabilities - Reconfigurability across mission profiles and unexpected orbit transfers - All-of-vehicle (bus and payload) autonomous optimization - Autonomous load-shedding method to maintain mission operation and payload prioritization under duress - Autonomous maneuver for collision avoidance Desired technologies may include: - Architectures enabling lifecycle extension - Modular tech, adaptive interfaces - Bus platforms designed to adapt to technological change without re-architecture - Integration-ready design for sensors, compute, and maneuver systems - Highly maneuverable design to include high-delta V, thermal management, and on-board power systems capable of running edge-computing capability Proposals must emphasize technical feasibility and clearly outline a Phase I research plan that includes modeling, simulation, literature review, or trade studies. While a Phase II transition concept may be outlined, Phase I deliverables are expected to focus on a feasibility study detailing USSF CONOPS and technical viability, as well as analytical findings, and early validation in collaboration with a university or research partner. ANNEX I: Guidelines for CONOPS development and Feasibility Study The Concept of Operations (CONOPS) should clearly communicate how the technology is intended to support a military mission within a defined system. The main purpose of the CONOPS is to facilitate a common understanding of a future system to help develop operational and system-level requirements. CONOPS should be written at the system level first and then expanded on how the technology functions enable successful military operations. The CONOPS needs to include, at a minimum: - The USSF mission and the mission military objectives - The problem being addressed by the technology in the context of the mission (e.g., existing capability gap) - (preferred) An operational overview diagram along with supporting text - (preferred) A system architecture overview diagram with all the elements needed to accomplish the mission to include - Integration-ready design for sensors, computing , and maneuver systems Operating Concept to Technology: Space Control and Battle Management are supported through total Space Domain Awareness. The first imperative is achieving persistent, predictive, and precise Space Domain Awareness (SDA). SDA must encompass not only tracking and characterization of objects in orbit, but also attribution of intent, behavioral analysis, and predictive targeting. This requires integration across commercial sensors, alongside AI-driven decision aids and real-time orbital maneuver detection. The processing of data from an ever-increasing coverage of in-space SDA is vital towards achieving decision and first-mover advantage. ANNEX II: Sample Key Performance Targets The following list sample key performance targets and descriptions for Edge Computing and Algorithms: - Autonomous Decision-Making: Demonstrate autonomous tracking of multiple maneuvering objects with no ground intervention - Data Fusion Latency: Fuse data from at least two sensor modalities (e.g. RD. IR, LiDAR) rapidly to generate a comprehensive situational awareness picture - Predictive Threat Accuracy: Attain a substantial rate in predicting threatening maneuvers of resident space objects (RSOs) with a false positive rate under 5% - Radiation Tolerance: The proposed computing hardware should be designed to withstand a realistic space radiation environment with ionizing doses expected between LEO to GEO to ensure suitable long-term operation (up to 100 krad(Si)) - Data Compression: Achieve a significant data compression ratio on sensor data to minimize data cross-link or downlink requirements, with no loss of critical information for assessment The following list sample key performance targets and descriptions for Sensor Payloads: - Persistent Custody: Maintain persistent, unbroken custody of a maneuvering RSO for a continuous period - Object Detection Sensitivity: Detect and track faint objects in LEO, MEO, GEO, or XGEO - SWAP-C Reduction: Achieve a reduction in Size, Weight, Power, and Cost (SWaP-C) compared to current generation sensor systems with equivalent or superior capabilities (industry standards) - Multi-Object Tracking: Simultaneously track and maintain custody of multiple resident space objects within the sensor's field of view. - Real-time Change Detection: Detect and characterize a change in a satellite's configuration or within seconds to several minutes The following list sample key performance targets and descriptions for Bus Design: - Autonomous Operations: The bus must be capable of operating autonomously for at least several days without any ground contact, performing all necessary station-keeping, power management, and collision avoidance tasks - Payload Power and Data: Provide continuous power and at least 10 Gbps data throughput to support multiple advanced sensor payloads and on-board processors simultaneously - Thermal Dissipation: The thermal control system must be capable of dissipating waste heat from high-performance computing payloads while maintaining all components within their operational temperature ranges Feasibility Study: A feasibility study is an assessment of the practicality of developing and implementing a new technology within a given concept. The study aims to objectively and rationally uncover the strengths and weaknesses of the new technology, the feasibility of successful development of the technology, and the viability of the success of implementation of the technology in the proposed CONOPS. The study should: - Identify the benefits of the new technology within the CONOPS over existing solutions (or lack thereof) - Identify the risks, uncertainties/unknowns, and issues of the proposed CONOPS with mitigation and closure measures - Identify the critical technology elements (CTEs) of the new technology - Provide data to support the feasibility of technology development of each CTE to include: literature research supporting technical viability or historical usage, trade studies identifying existing technologies, current developments, and/or feasible development pathways, analysis demonstrating technical solutions, and heritage usage of similar technology with identified differences in operations and/or environment for proposed CONOPS. Keywords: Space Domain Awareness (SDA); Space Control (SC); Space Battle Management (SBM); Passive RF; LIDAR; Radar Detection Technologies; Next-Gen Sensor Solutions; Machine Learning; AI; Radiation-Hardened; Real-Time Data Processing; Rapid response