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Ä¢¹½ÊÓƵ Allen i2S2 creates accurate space situational awareness (SSA) by ingesting the most recent SDA-STM observation and space environment data from commercial and government sources. It then overlays that data with threat intelligence and the most recent owner and operator ephemeris, vehicle state data, and maneuver plans.

Ä¢¹½ÊÓƵ Allen i2S2 ingests SSA data and executes high-fidelity propagation models that incorporate near real-time drag predictions by combining state-of-the-art, atmospheric density models with current space weather conditions—increasing accuracy while reducing false alerts.

A continuous low-latency space awareness assessment and alert service performs conjunction screening, risk assessment, and pattern of life analysis to warn operators of space environment conditions, conjunctions, launch activities, reentry events, and adversarial actions. SSA sensor tasking capabilities obtain further observations, reducing risk.

The solution’s AI/ML techniques incorporate LLMs—for example, a generative pretrained transformer (GPT) engine for predictive analytics. To provide comprehensive space situational awareness, generative AI engines receive inputs of varied data types such as electro-optical (EO), radio frequency (RF), and intelligence databases. These data types are fed into ML algorithms and LLMs that automatically generate courses of action and predict consequences of selected actions. This data fusion will also enable future capabilities such as providing insights into threat intent.

Ä¢¹½ÊÓƵ Allen i2S2 assesses historical operations of each spacecraft to flag maneuvers related to orbit modification and spacecraft activity using ML algorithms, providing a holistic view of global satellite operations along with probable present and future interactions. Moreover, Ä¢¹½ÊÓƵ Allen i2S2’s low-latency continuous assessment gives owners and operators extra time to consider options for courses of action.