CubeSat Cluster Deployment Tracking

Clustered CubeSat deployments, where dozens of CubeSats are released over a short time span, represent a relatively new and challenging problem for detection, tracking, and space traffic management. Over the last few years CubeSat missions have reported on the difficulty of relying on Two Line Elements (TLEs) provided by the Joint Space Operations Center (JSpOC) early in the mission. Researchers and industry professionals have begun taking an interest in this scenario. Mainly they are advocating policy changes to push CubeSat developers toward adding navigation aids, in the form of ID beacons or reflectors. We propose to develop a robust solution that leverages, but is not entirely reliant on compliance by CubeSat developers. Our goal is to develop and demonstrate a resilient strategy for the deployment, detection, and tracking of multiple CubeSats. The proposed research for the first year focuses on solving the estimation problem of detecting, tracking and identifying each individual CubeSat in a realistic large scale deployment scenario. Multi-­‐target estimation methods based on random finite set (RFS) statistics, including the cardinalized probability hypothesis density (CPHD) and the labelled multi-­‐Bernoulli (LMB) filter provide the basis for this work. Using the understanding gained from the first year study, the second year will focus on recommending deployment strategies and accelerating the estimation process, using reported data from a subset of cooperative, fully functioning CubeSats in the deployment. The result will be a validated set of algorithms to support and enhance the safety and speed at which future CubeSat deployments can achieve their mission goals.


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Filing Info

  • Accession Number: 01678256
  • Record Type: Research project
  • Source Agency: Federal Aviation Administration Center of Excellence for Commercial Space Transportation
  • Files: RIP, USDOT
  • Created Date: Aug 22 2018 1:03PM