DAF27BX02-NV501 — Battle Damage Indications / Battle Damage Assessment Collection, Aggregation, and Analytics Improvement

To accelerate decision-making and enhance space domain awareness across the joint force, the United States Space Force (USSF) requires an advanced data fusion and automated analytics platform. This effort will deliver integrated collection, aggregation, and analytics software capabilities that automate multiple heterogeneous sensor and telemetry sources and telemetry sources into a unified Battle Damage Indication and Assessment (BDI/A) data store Ontology to optimize the Tasking, Collection, Processing, Exploitation, and Dissemination (TCPED) lifecycle. Targeted solution will execute automated cross-domain event correlation, implement a dynamic confidence scoring framework with user-adjustable thresholds, provide explainable Artificial Intelligence and Machine Learning (AI/ML) diagnostics reaching >80% agreement with Government analysts, and supply a role-based user interface supporting tactical, operational, strategic, and Intelligence Community (IC) workflows.

To preserve decision advantage during contested-space operations, the United States Space Force (USSF) must rapidly correlate multi-source operational data to streamline Phase 5 (Mission Planning & Force Execution) and Phase 6 (Assessment) of the Space Tasking Cycle (STC) and Joint Targeting Cycle (JTC). The Space Systems Command (SSC) System Delta 85 (SYD 85) Program Management Office (PMO) and the Kronos Program of Record (PoR), in support of the Battle Management, Command, Control, Communications, and Space Intelligence Portfolio Acquisition Executive (BMC3I PAE), is seeking software-based solutions for the BMC3I TAP Lab (Tools, Applications, & Processing) to automate, aggregate, and score multi-phenomenology collection activities in a standardized, Unclassified environment designed for the eventual seamless scaling to classified operational networks. Within this request, there are 5 associated subtasks: Automated ingestion and schema mapping of multiple heterogeneous sensor and telemetry sources telemetry and sensor sources into a common BDI/A data store Ontology definition model while preserving metadata and timestamp traceability; Cross-domain synchronization and analytics of Electro-Optical/Infra-Red (EO/IR), Radio Frequency (RF), and Radar data products (such as resolved/unresolved observations, Doppler Residuals, and Area-to-Mass Ratio datasets) to detect pre-defined anomalous behaviors using configurable threat signatures; Execution of a dynamic, multi-phenomenology confidence scoring framework that evaluates collection source reliability and sensor quality metrics; Development of user-friendly, explainable AI/ML-assisted correlation diagnostics that expose the underlying analytical rationale to human operators to assist in target adjudication; and Delivery of a segmented user interface enabling Subject Matter Experts (SMEs) to isolate and execute workflows across the four distinct echelons of the BDI/A lifecycle (Tactical BDI, BDA Phase 1, BDA Phase 2, and BDA Phase 3). Sample inputs include; - Real-time or historical feeds; - Ground- and space-based multi-phenomenology sensor data (commercial and government); - Historical event repositories for change detection; and - Simulated operational event logs. Possible techniques may include but are not limited to; - Automated schema mapping and metadata parsing; - Change detection algorithms against historical patterns of life; - Multi-hypothesis data association and Kalman filtering; and - Explainable AI (XAI) natural language explanation generation. The software product must be designed to integrate with existing and legacy Mission Unique Software (MUS) toolchains, publishing assessments back to source repositories via standardized application programming interfaces (APIs) and meeting all development requirements for future transition to secure operational networks.

: Phase I will focus on establishing the scientific, technical, and operational feasibility of the proposed automated, multi-phenomenology BDI/A collection, aggregation, and analytics capability. The primary objective is to validate the product-mission fit and construct a high-fidelity conceptual architecture for the unified BDI/A data store Ontology and cross-domain correlation engine. During the Phase I feasibility study, the small business concern (SBC) must: Identify and collaborate with specific United States Space Force (USSF) and U.S. Air Force stakeholders, operational end-users, and technical transition partners (aligning with Space Forces-Space (S4S), National Space Defense Center (NSDC), Combined Space Operations Center (CSpOC), Space Force Deltas, and the Intelligence Community) to map existing BDI/A workflows, data formats, and classification boundaries. Develop a comprehensive technical architecture and data schema for the proposed BDI/A Ontology, outlining how the system will ingest and normalize multiple heterogeneous sensor and telemetry sources and sensor sources (such as TT&C, RF, optical, and radar datasets) without losing source metadata or timestamp traceability. Define the algorithmic approach for multi-phenomenology sensor fusion, change detection, dynamic confidence scoring, and explainable AI/ML correlation models, detailing how the software will communicate its analytical reasoning to human operators to achieve >80% agreement with Government analysts. Create an integration and cybersecurity plan detailing how the proposed capability will interface with legacy and novel Mission Unique Software (MUS), comply with Cybersecurity Maturity Model Certification (CMMC) frameworks, and transition from an Unclassified proof-of-concept environment to TS//SCI Classified networks. Formulate a commercialization strategy demonstrating how these decentralized analytics, multi-source fusion algorithms, and diagnostic user interfaces can be commercialized for private space operators, commercial satellite fleet managers, and space logistics providers. The Phase I effort must culminate in a detailed feasibility study report and a low-fidelity mock-up or architectural software proof-of-concept validating user experience flows for the four distinct echelons of the BDI/A warfighting lifecycle.

Phase II will focus on developing, refining, and validating the software capabilities outlined in the Phase I feasibility study within the BMC3I TAP Lab testing environment. Specifically, Phase II aims to deliver the fully functional BDI/A data store Ontology, multi-phenomenology collection and aggregation engine, and dynamic confidence scoring framework, maturing the overall solution to a target technology readiness level (TRL) 7/8. The development process will require multiple heterogeneous sensor and telemetry sources, establishing automated schema mapping, and implementing real-time event correlation algorithms. The software must prove capable of detecting pre-defined anomalous signatures using configurable threat parameters, displaying event sequencing dependencies, and enabling deep-dive drill-down traceability to the originating raw data records. Evaluators will test the system's performance using realistic historical and simulated operational scenario runs, verifying that the AI/ML correlation recommendations and dynamic scoring outputs achieve >80% agreement with validated Government analyst assessments. The final operational assessment will require demonstrating thirty (30) days of continuous, high-availability operations on representative networks to ensure seamless integration with the Kronos PoR. The platform will undergo sustained operational assessments on representative networks, establishing a verified baseline of operational reliability and mission-essential resilience.

Potential Phase III efforts will focus on scaling and transitioning the matured BDI/A collection, aggregation, and scoring capabilities directly to operational joint targeting and space tasking systems under designated Programs of Record. Successful solutions will transition to TS//SCI Classified environments to automate target adjudication and operations assessment reporting for S4S, CSpOC, NSDC, USSC, and their IC partners. This ensures a rapid, automated flow from tactical BDI events to strategic and national-level BDA Phase 1, 2, and 3 intelligence outputs, significantly accelerating the targeting cycle loop. Additionally, the technologies developed under this topic have dual-use potential within the commercial space sector. Commercial satellite fleet operators, mega-constellation managers, and space situational awareness (SSA) service providers require robust, automated systems to conduct vehicle health diagnostics and anomaly resolution. When commercial satellites experience sudden subsystem dropouts or structural degradation (due to orbital debris collisions, space weather events, or local RF interference), operators must quickly assess the damage, isolate the cause, and adjust operational risk postures. Applying this multi-source data fusion, change detection, and explainable AI-assisted scoring algorithms allows commercial operators to automate damage assessment pipelines, lower telemetry analysis overhead, and streamline insurance claim validations, directly increasing the safety and survivability of commercial orbital assets.