Automating Correlation of Unidentified Tracks with Known Objects

Every day, global sensor networks produce thousands of new, uncorrelated tracks (UCOs). Manually associating these with known catalog objects is a slow, error-prone process performed by analysts, creating a latency that directly increases collision risk. This catalog drift—where objects are misidentified or remain unknown—degrades the accuracy of all downstream conjunction assessments. Automating this correlation is not an AI experiment; it is an operational necessity to achieve the data fidelity required for safe, scalable space operations.

A production-grade automation workflow ingests tracks from sources like Space Fence, optical telescopes, and commercial feeds into a normalization layer. A correlation engine, using orbit determination and statistical scoring, attempts matches against the catalog. High-confidence updates proceed automatically; ambiguous cases are routed to a prioritized human review queue with supporting data. This closed-loop system, integrated with systems like SOCRATES, reduces analyst workload by over 70% while providing the clean, current catalog required for high-fidelity risk scoring and maneuver planning.

This workflow directly targets the operational bottleneck of manual track correlation, where analysts spend hours matching new sensor 'tracks' to cataloged objects. Automating this process reduces analyst workload by over 70%, accelerates catalog updates, and improves the fidelity of conjunction risk assessments. The business value is clear: faster, more accurate correlation means earlier identification of uncatalogued debris and reduced risk of missed threats to operational assets, directly protecting revenue-generating satellites.

Implementation integrates specialized agents using LangGraph for orchestration, connecting to propagator libraries (e.g., Orekit, GMAT) and existing catalog databases. The architecture includes critical controls: confidence scoring to filter low-quality data, an approval gate for ambiguous cases, and immutable audit logs for every correlation decision. Deployment requires phased rollout, starting with high-altitude regimes to validate matching logic before full-scale ingestion, ensuring system reliability and analyst trust from day one.

The business case centers on operational leverage: manually associating new sensor observations ('tracks') with a catalog of millions of objects is slow, error-prone, and diverts expert analysts from higher-value threat assessment. Automating this correlation directly reduces the time-to-catalog new debris from hours to seconds, improving the fidelity of conjunction risk alerts for satellite operators. The initial phase focuses on ingesting and normalizing data from disparate sources like Space Fence radars and optical telescopes, establishing a clean pipeline for the correlation engine.

Implementation requires a multi-agent architecture. A correlation engine, using algorithms like Simplified General Perturbations (SGP4) propagators and statistical gates, compares new tracks against known orbits. Low-confidence matches are routed to a human-in-the-loop review console with prioritized context. The final phase integrates this workflow with downstream systems like conjunction assessment risk analysis (CARA) tools and operator dashboards, ensuring catalog updates immediately improve operational decision-making. Governance controls include match-confidence thresholds, audit logs for all catalog changes, and periodic model retraining on analyst overrides to reduce false positives over time.

Automating track correlation directly reduces the manual analyst hours spent on data association, a repetitive task consuming 30-40% of an SSA team's capacity. The business value is catalog integrity and accelerated threat identification, which lowers collision risk and protects high-value assets. Implementation requires a phased rollout, starting with a shadow-mode agent that logs its correlation decisions against human analysts to validate accuracy and tune confidence thresholds before assuming any operational responsibility. This initial phase builds trust in the automated logic while quantifying the potential labor savings.

Governance is enforced through a centralized dashboard monitoring match accuracy, false-positive rates, and analyst override frequency. Controls include configurable confidence thresholds for auto-acceptance and rules-based escalation for tracks near protected zones or high-value assets. The final rollout phase integrates the correlation engine directly with operational systems like AGI's STK or custom propagators, but retains a mandatory human-in-the-loop approval gate for any catalog creation or modification, ensuring an immutable audit trail for regulatory and liability purposes. This staged approach de-risks deployment while capturing the operational upside of automated correlation.

This automation directly addresses the operational bottleneck of manually correlating thousands of daily sensor observations with a catalog of millions of objects. The business value comes from maintaining catalog integrity, which is essential for accurate conjunction risk assessment and maneuver planning. By automating this tedious correlation, operators reduce analyst screening time by over 70%, prevent catalog drift, and ensure downstream collision avoidance systems operate on clean, current data. The solution relies on track-orbit correlation algorithms, often using Gaussian mixture models or multiple hypothesis tracking, integrated with systems like AGI's STK or custom propagators.

Implementation integrates with SSA data pipelines from sources like Space-Track.org or commercial feeds, normalizing TLE/OD data streams. The architecture includes an orchestrator, such as a LangGraph or custom multi-agent system, to manage the correlation logic, confidence scoring, and routing of ambiguous cases. Critical controls include setting correlation thresholds, maintaining an immutable audit trail of all associations, and implementing human review gates for low-confidence matches or potential new break-up events. Observability is provided through dashboards tracking correlation rates, false-positive volumes, and analyst backlog.