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Proceedings Paper

Large constellation tracking using a labeled multi-Bernoulli filter
Author(s): Nicholas Ravago; Akhil K. Shah; Sean M. McArdle; Brandon A. Jones
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Paper 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.

Paper Details

Date Published: 27 April 2018
PDF: 20 pages
Proc. SPIE 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII, 106460F (27 April 2018); doi: 10.1117/12.2304884
Show Author Affiliations
Nicholas Ravago, The Univ. of Texas at Austin (United States)
Akhil K. Shah, The Univ. of Texas at Austin (United States)
Sean M. McArdle, The Univ. of Texas at Austin (United States)
Brandon A. Jones, The Univ. of Texas at Austin (United States)


Published in SPIE Proceedings Vol. 10646:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII
Ivan Kadar, Editor(s)

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