Transforming Observations into Operational Intelligence

Slingshot’s advanced processing and fusion tools transform raw orbital data into actionable space awareness. By correlating multi-sensor inputs, determining orbits, and assessing predicted conjunctions, these capabilities form the analytical backbone of Slingshot’s operational infrastructure.

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Services

Multiple Frame Assignment Space Tracker as a Service (MFASTaaS)

MFASTaaS fuses multi-phenomenology sensor observations to produce high-quality orbital state vectors through a hosted data fusion engine based on Slingshot’s government- and industry-validated MFAST software. MFASTaaS provides users with reliable, frequently updated fused orbital state estimates on both known and unknown objects — essential for conjunction analysis and follow-on actions (e.g., maneuver planning). This enables:

Near–real-time orbital solutions for cataloged and newly discovered objects
Flexible support for integrating internal and external observations
Service delivery through APIs or within the Slingshot Platform
Quality metrics and uncertainty bounds for propagation

PropaaS — Cloud-Based Ephemeris Generation

Propagation as a Service (PropaaS) allows users to generate predicted ephemerides on demand using Slingshot’s internal high-fidelity orbital propagation models and provided initial state data. PropaaS accelerates mission operations by giving users control over their own orbital propagation-based workflows. This enables:

Ephemeris generation using GNSS-based state inputs, state vectors, or uploaded TLEs
Rapid forward modeling for mission planning or predictive analyses
Integration with MFAST outputs for propagation continuity
Batch-mode support for large satellite catalogs

Tools

Multiple Frame Assignment Space Tracker (MFAST)

Multiple Frame Assignment Space Tracker (MFAST) is Slingshot’s advanced engine for near–real-time multi-sensor, multi-object tracking. It supports uncorrelated track (UCT) processing, initial and orbit determination (IOD/OD), and maneuver detection. Core capabilities include:

Rapid UCT resolution and object discovery
IOD/OD for establishing and maintaining custody
Breakup and closely spaced object processing
Maneuver detection and support for catalog-quality updates
Scalable multi-sensor fusion for near–real-time tracking

High-Fidelity Propagation Engine

Built on implicit Runge–Kutta numerical methods, this engine delivers dynamic, physics-based orbital propagation with high fidelity — including support for drag modeling and maneuver insertion. IRKProp is used to support mission-relevant forecasting workflows, near–real-time tracking updates, and risk analysis. This includes:

Precise propagation of orbital trajectories across all regimes
Includes support for high-fidelity drag modeling in LEO (enhanced LEO drag model coming soon).
Support for maneuver modeling using burn parameters or uploaded commands
Error-growth modeling and scenario analysis

Conjunction Assessment Toolkit

This modular engine provides conjunction assessment and probability of collision estimation, and can process CDMs, VCMs, and raw ephemerides. It underpins Slingshot’s conjunction alerts, supports Agatha AI with contextual orbital data, and enables automated fleet-monitoring workflows. This enables:

Customizable screening volumes and thresholds
Probability of collision (Pc) estimation across multiple uncertainty models
Integration with sovereign or external catalogs
Ephemeris-vs-CDM-based pairwise screening pipelines