Slingshot Capabilities

Slingshot has developed 8 proprietary day and nighttime capable optical sensor systems operating at ultra-violet, visible, and shortwave infrared wavelengths. These systems include narrow and wide field-of-view taskable telescopes and wide field-of-view staring arrays.

Spanning 20 sites and counting, the Global Sensor Network has geographically diverse sites to mitigate weather outages, provide redundant observation opportunities, and enable high revisit rates for improved custody.

Slingshot's patented infrared daytime sensors allow for 5x the number of observation opportunities against LEO objects compared to nighttime-only systems. This same daytime capability also reduces custody gaps for GEO spacecraft.

Slingshot's optical sensor systems are powered by proprietary algorithms and software that enable the low-latency production of high-quality data products at scale.

Slingshot’s Tasking, Collection, Processing, Exploitation, and Dissemination (TCPED) turns this network of telescopes into a set of priority-scheduled and persistent data collectors.

Slingshot’s continues to expand the Slingshot Global Sensor Network with plans to reach over 200 deployed sensors in 2024. The expansion will increase Slingshot’s LEO observations by 100x, generating over 1,000,000 LEO observations per day.

Narrow and wide field-of-view gimbaled telescopes track high-priority objects from LEO to xGEO, day and night.

Multi-camera sensor systems provide persistent, uncued nighttime tracking of spacecraft and debris from LEO to GEO.

Our uncued staring optical staring array systems catch LEO and GEO activity as it unfolds – making millions of observations available via our archive for post-event analysis.

Built specifically for space surveillance tracking, Slingshot’s Multiple Frame Assignment Space Tracker (MFAST) software is being used daily by the US Space Force and other partners to process observations collected by multiple sensor networks.

Customized astrodynamics and multi-hypothesis tracking algorithms provide scalability and highly-accurate, near real-time orbits for government and commercial use cases.

Track objects in all orbital regimes using radar, passive RF, ground-based optical, and space-based optical sensors.

Slingshot MFAST is used operationally by government customers around the globe to quickly resolve uncorrelated tracks (UCTs) that result from large maneuvers, breakup events, and new launches.

Customized astrodynamics and multi-hypothesis tracking algorithms provide scalability and highly-accurate, near real-time orbits for government and commercial use cases.

Physics-based complexity reduction methods allow MFAST to be run on most platform and hardware setups, even laptops.

Slingshot has developed a physically high-accuracy astrodynamics simulation engine (PHASE) with the ability to provide insights into past, present, or potential future conditions. The engine simulates entire constellations of satellites, and interactions among objects and sensors, for multi-year time periods.

• High-fidelity representation of orbital environment and ground sensors
• Physically accurate environment for operating simulated vehicles and constellations
• Realistic uncertainty and noise generation for all data streams

Create intuitive visualizations that include space assets, ground assets, space and terrestrial weather, and physics-true orbital mechanics.

PHASE serves as a generation engine for testing new insight products, for evaluating the effectiveness of equipment acquisitions and sensor placement, and for assessing constellation-level emergent properties.

Build a model of planned operations, including potential behaviors of other operators, to evaluate potential courses of action and alternative scenarios.

Slingshot's PHASE powers the large scale simulation framework and models necessary to create the requisite synthetic training environment in which AI agents are trained, capturing the many and complex interactions between space-based objects and terrestrial sensors.

• Autonomous AI agents can be used to test advanced courses of action across a range of simulated conditions in order to identify the most effective approaches for real-life missions – improving the operational readiness of all trainees.
• Agents trained with realistic physics enable evaluation of counterfactual scenarios without any risk to real-world assets.

• AI agents serve as readily available adversaries in training exercises, with the ability to replicate real-word challenges or plausible future scenarios.
• The physics-based simulation environment ensures that AI agents are trained with knowledge applicable to real-world challenges, and that they act in accordance with natural laws of space.

Scalable AI agent training system lends the ability to rapidly train and deploy agents for  numerous simulated scenarios.

Configurable development pipeline enables case-specific AI agent creation for a range of applications.

Our Orbital Analyst Services provide remote and on-premises support to customers leveraging Slingshot Global Sensor Network data to advance their missions.

Slingshot’s astrodynamics team supports a range of customer needs on a contract basis with a focus on tackling challenges that require advanced astrodynamics knowledge and expertise.

Slingshot’s software engineering services give customers access to Slingshot’s talented frontend and backend engineers with a wealth of experience building applications and APIs for space operations.