Machine Learning to Detect Orbital Debris for IARPA
Machine Learning to Detect Orbital Debris for IARPA
Advanced Space Working with Office of the Director of National Intelligence
Advanced Space LLC., a space tech solutions company, will lead a team that will apply Machine Learning (ML) capabilities to detect, track and characterize space debris for the IARPA Space Debris Identification and Tracking (SINTRA) program.
“Monitoring orbital debris is critical to the sustainable exploration, development and settlement of space."
Bradley Cheetham, Advanced Space
The IARPA SINTRA program aims to investigate the interaction of orbital debris with the surrounding space environment and drive the state of the art to detect, track, and characterize lethal non-trackable orbital space debris. The Intelligence Advanced Research Projects Activity (IARPA) invests in high-risk, high-payoff research programs to tackle some of the most difficult challenges of the agencies and disciplines in the Intelligence Community (IC).
Space debris—items due to human activity in space—presents a major hazard to space operations. Advanced Space and its teammates Orion Space Solutions and ExoAnalytic Solutions are applying advanced ML techniques to finding and identifying small debris (0.1-10 cm) under a new Space Debris Identification and Tracking (SINTRA) contract from Intelligence Advanced Research Projects Activity (IARPA).
“Space debris is an exponentially growing problem that threatens all activity in space, which Congress is now recognizing as critical infrastructure,” said Principal Investigator Nathan Ré. “The well-known Kessler syndrome will inevitably make Earth orbit unusable unless we mitigate it, and the first step is developing the capability to maintain persistent knowledge of the debris population. Through our participation in the SINTRA program, our team aims to revolutionize the global space community's knowledge of the space debris problem.”
Currently, there are over 100 million objects greater than 1 mm orbiting the Earth; however, less than 1 percent of the debris that could cause mission-ending damage are currently tracked. The Advanced Space team’s solution—the Multi-source Extended-Range Mega-scale AI Debris (MERMAID) system—will feature a sensing system to gather data; ground data processing incorporating ML models to observe, detect, and characterize debris below the threshold of traditional methods; and a catalog of this information. A key component of this solution is that the team will use ML methods to decrease the Signal-to-Noise-Ratio (SNR) required for detecting debris signatures in traditional optical and radar data.
“Monitoring orbital debris is critical to the sustainable exploration, development and settlement of space," said Advanced Space CEO Bradley Cheetham. "We are proud of the work the team is doing to advance the state of the art by bringing scale and automation to this challenge.”