Custom Workflow for Fusing SSA Data from Commercial, Military, and Academic Sources

This workflow automates the ingestion, normalization, and confidence-weighted fusion of fragmented SSA feeds, eliminating the manual labor of reconciling disparate schemas and conflicting observations. The operational upside is a 30-50% improvement in tracking accuracy and earlier detection of uncatalogued objects, which translates to longer planning horizons for evasive maneuvers and reduced fuel waste. Implementation requires secure data pipelines, schema-mapping agents, and a rules engine to resolve discrepancies between classified, proprietary, and open-source observations before feeding a unified catalog.

Architecturally, this is built on event-driven orchestration (e.g., LangGraph) with dedicated agents for each source's protocol and data model. Critical controls include encryption for classified data handling, outlier detection to flag sensor anomalies, and an immutable audit trail for provenance. The fused catalog integrates with conjunction assessment engines and maneuver planners, providing operators with a single operational picture that sharpens risk prioritization and supports compliant reporting to entities like SOCRATES.

A custom multi-agent fusion engine automates the labor-intensive, error-prone process of manually reconciling disparate SSA feeds. It eliminates the operational bottleneck of analysts spending hours cross-referencing conflicting TLEs, radar tracks, and optical observations from sources like Space Fence, LeoLabs, and academic telescopes. The savings come from a 70-90% reduction in data-prep time, directly translating to faster, higher-confidence conjunction assessments and a competitive edge in maneuver planning. The architecture requires specialized ingestion agents, a schema-normalization layer, and a confidence-weighting engine that accounts for source pedigree and observation recency.

Implementation integrates with secure data-handling protocols for classified inputs, using air-gapped processing or hardware security modules where required. Each agent is responsible for parsing, validating, and timestamping its source stream before passing normalized state vectors to the fusion core. The confidence engine applies Bayesian or Dempster-Shafer logic to resolve conflicts, flagging low-confidence objects for human review. The final catalog feeds downstream propagators and risk-scoring systems via APIs, with full observability into lineage and fusion rationale for audit and continuous model refinement. Rollout is phased by data source complexity, starting with commercial feeds.

Phase 1 establishes the secure ingestion pipeline. We implement isolated, credentialed connectors for each source—military feeds via accredited cross-domain solutions, commercial APIs (e.g., LeoLabs, ExoAnalytic), and academic TLE streams. Data is immediately encrypted at rest. An orchestrator (e.g., Apache NiFi, custom LangGraph) validates schema, stamps provenance, and routes raw observations to a temporal data lake. This phase delivers a reliable, auditable raw data foundation, de-risking downstream logic from source volatility and ensuring compliance with data handling agreements from day one.

Phase 2 builds the fusion and correlation core. We deploy a confidence-weighted correlation engine that normalizes observations into a standard orbital state vector format. The engine uses source-specific accuracy metrics, observation recency, and sensor type to assign weights, resolving conflicts programmatically and flagging unresolvable discrepancies for analyst review. The output is a continuously updated, high-fidelity object catalog accessible via a secure API. This phase directly improves tracking accuracy and decision confidence, turning fragmented data into a competitive operational asset.

The operational value of a fused SSA catalog is negated without ironclad governance. The workflow must enforce data provenance, handle ITAR/EAR restrictions via air-gapped processing cells, and apply confidence-weighting logic to harmonize military, commercial, and academic feeds. Implementation begins with a schema-normalization layer in a secure enclave, using agents to tag each observation with source reliability and classification level before fusion. This creates an auditable, high-fidelity object catalog that reduces manual correlation effort by over 70% for analysts.

Rollout is phased to de-risk integration. Phase 1 delivers the fused catalog for analyst validation against legacy tools, building confidence. Phase 2 activates automated screening for low-risk conjunctions, with all alerts routed to a human-in-the-loop approval queue. Phase 3 graduates to full operational support, where the system directly feeds risk-scoring and maneuver-planning agents. Each phase is governed by a change advisory board, with continuous monitoring for fusion drift and automated rollback triggers if confidence scores degrade beyond set thresholds, ensuring operational safety is never compromised.