Large constellation tracking using a labeled multi-Bernoulli filter

Nicholas Ravago; Akhil K. Shah; Sean M. McArdle; Brandon A. Jones

doi:10.1117/12.2304884

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27 April 2018 Large constellation tracking using a labeled multi-Bernoulli filter

Nicholas Ravago, Akhil K. Shah, Sean M. McArdle, Brandon A. Jones

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Proceedings Volume 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII; 106460F (2018) https://doi.org/10.1117/12.2304884
Event: SPIE Defense + Security, 2018, Orlando, Florida, United States

Abstract

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.

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Nicholas Ravago, Akhil K. Shah, Sean M. McArdle, and Brandon A. Jones "Large constellation tracking using a labeled multi-Bernoulli filter", Proc. SPIE 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII, 106460F (27 April 2018); https://doi.org/10.1117/12.2304884

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