Large constellation tracking using a labeled multi-Bernoulli filter
Nicholas Ravago, Brandon A. Jones, Akhil K. Shah, Sean McArdle · 2018
Multiple companies have recently proposed or begun work on large constellations of hundreds to thousands of satellites in low-Earth orbits for the purpose of providing worldwide internet access. The sudden infusion of so many satellites in an already highly-populated orbital regime presents an operational risk to all LEO objects. To enable risk analyses and ensure safe operations, a robust system will be needed to efficiently observe these constellations, and use the resulting data to accurately and precisely track all objects. This paper proposes a rudimentary tasking-tracking system for this purpose. The scheduler uses an information theoretic reward function to determine which high-value tasks, and uses a ranked assignment algorithm to optimally allocate these tasks to a sensor network. The tracking portion employs a labeled multi-Bernoulli filter to process the generated data and estimate the multitarget state of the entire constellation. The effectiveness of this system is demonstrated using a simulated large constellation of 4,425 satellites and a network of six ground-based radar sensors.