SPACE TRAFFIC MANAGEMENT
What we could do
Objects in orbit
Tracked Objects are as small as a
Objects are identified and monitored by the
Our best observational
data is limited to snapshots over time from our sensor network.
These snapshots must be processed to know which object is which and
what it is doing.
Advanced Analytics to the Rescue
www.Space-Track.org helps U.S. Space Command facilitate Space Domain Awareness globally
• A safer space environment benefits everyone
Through advanced analytics of orbit data, we can derive trends for each space object
These trends help us generate a pattern of life for space objects
Helps determine when an object is displaying irregular behavior that could mean trouble, such as when the object has struck something and is about to break up
Using these patterns and by comparing historical and trend data, it may be possible to understand uncategorized objects and enhance space traffic tracking fidelity
Data Science is at the intersection of statistics, computer science, and subject matter experts.
SAIC’s data science team was given 30 days and 80 funded hours to learn what insights they could derive from a few .txt files of space object data from space-track.org. Little did they know, inputs from satellite tracking experts would be disrupted by the onset of COVID-19. Regardless, the data team was able to clean the data for comparison, identify outliers, create clusters, and visualize the data. If actionable insight equals data plus resources (time, money, expertise, etc.), imagine what SAIC team can do with a data set, a few hours of expert inputs, and a few weeks of funded labor?
The team was given 130 million-plus rows of two-line element (TLE) data files containing orbital elements of space objects (23 .txt files with TLE data spanning 18.6 GB)
Out of the 45,000 Satellites in the data set we were given five ID numbers as examples of what is considered bad
The team developed a custom parser to help clean the data. Once cleaned, SAIC data scientists ingested the data to create a dynamic database, all web browser based, from which they sliced and diced off any criteria and queried in near real-time
The team leveraged open source tool called Cesium that they used to containerize and show the possibility of displaying the results in a 3D viz tool
In the end, the team was able to produce fairly obvious clusters to the human eye in the viz tool, and have some preliminary methods to detect outliers in TLE timelines
Docker containers enable data science and the visual tool
GitHub Repo delivers tools for ingesting, cleaning, and creating a database that space operators can query dynamically
Visualization illustrates figures, animations, etc.
Process gathers a consensus of clusters through unsupervised clustering
Outlier detection code helps find anomalies
What We Could Do
Develop a pipeline to integrate the space_viz tool with the data science process for a more interactive experience
Work with your SME to understand more about what makes up
the particular problem they're trying to solve
Build predictive models that answer your particular
Leverage the additional data in Space-Track about the Sat Cats to gain better clustering or labels to train AI models against
Diagnose malfunctioning satellites - When the algorithm detects a deviation in the pattern of life, we can set up alarms to notify interested parties. This is when using boosters may help
Now imagine the possibilities
Combining machine intelligence with human intelligence is what leads to gaining new insights. The sum of AI and human expertise is great insight created faster. SAIC is now looking at other areas to explore the art of the possible. Stay tuned to see what we discover next.
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SPACE TRAFFIC MANAGEMENT
Space traffic management is complex. Thousands of man-made objects orbit the earth, ranging from space debris to GPS satellites to the International Space Station. Keeping track of all of them is the U.S. Space Force’s space-track.org program. SAIC data scientists asked what insights they could gain from a space-track.org data dump and a few weeks of data analysis, applying Artificial Intelligence and Machine Learning. Learn what they discovered.