3 Accelerators for Space Domain Awareness

The race is on to develop space capabilities for a growing array of national security, civil, and commercial priorities. With the satellite population expected to reach as many as 100,000 assets by the end of the decade, the challenge of keeping track of all those orbital objects and associated debris is formidable. Add analysis for critical missions, and it’s clear that space domain awareness needs to transform faster.

While details on 18th Space Defense Squadron operations and the transition of civil space traffic management (STM) to the National Oceanic and Atmospheric Administration (NOAA) Office of Space Commerce (OSC) are being finalized, stakeholders can focus on evolving technical capabilities by doubling down on data.

Here are three areas where forward-looking space organizations can move ahead—using technologies that are available today.

Fusing data from satellites across orbits onto one platform is essential for the emerging space ecosystem. Pulling in sensor data from partners around the globe—such as radar, optical, microwave radiometer, and infrared—and integrating it with data such as self-reported ephemerides will enable a new level of precision.

Think of the information national security experts will reap by adding unclassified data from commercial space service providers, civil agencies such as NASA and NOAA, and diverse signals intelligence—all curated and moved into a classified multi-intelligence framework. Likewise, civil agencies like OSC as well as commercial companies will gain more detailed data on environmental changes in remote areas, for example, by gaining access to declassified intelligence data. And all space organizations will capture the benefits of planning their missions with greater confidence.

Yesterday’s models won’t work for tomorrow’s missions. Take collision avoidance, for example—a key use case for space traffic management. In the past, when there were fewer space assets and more predictable maneuvers, simple drag models, propagation algorithms, and gravitational models sufficed for space traffic managers to assure collision avoidance. Now, with an estimated 670,000 objects larger than 1 cm floating in space, and thousands of programmable objects moving in and out of orbit¹, operators need to know precisely where their assets are relative to everything else.

Aggregating and fusing the data will enable new artificial intelligence (AI)/machine learning (ML) algorithms, accelerated with powerful graphical processing units for increasingly sophisticated, highly accurate modeling and simulation. For example, factoring in data on solar effects and the calculations of space object motion by powerful astrodynamics engines will help data scientists build accurate drag models for space objects traveling through the upper ionosphere.

These drag models will, in turn, inform sophisticated propagation models developed for varied use cases. Analysts and operators will receive more accurate data faster—operators only partially comply with maneuvers from a conjunction event (e.g., only 50% of operators maneuver when given a conjunction warning²).

Knowing where an object will be at a future date will give stakeholders the advantage of acting preemptively (for example, to correct or optimize a flight path). It also will enable trend analysis, for example, to determine intent when surveilling the behavior of a rival nation’s satellite.

This also enables automation, which will be critical for fast response. In the past, agencies needed to know of events that might occur next week—now, they need to know what can happen in the next moment. Therefore, space agencies will need not only automated alerts but also automated courses of action. And all must be delivered via user-friendly, intuitive interfaces.