Large-Scale Space Object Tracking in a Proliferated LEO Scenario

The proliferation of large satellite constellations in low Earth orbit (LEO) is dramatically increasing demand on existing systems for space domain awareness. The rapidly growing number of objects in LEO will reduce the average rate of observations per object, necessitating the development of multi-target algorithms that can handle higher levels of data sparsity without sacrificing computational efficiency. In this paper, we demonstrate that a multi-target filter that combines a number of useful capabilities is able to track and maintain custody of a simulated population of over 16,000 LEO objects without requiring the use of high-performance computing facilities. The filter is based on the generalized labeled multi-Bernoulli filter, and includes three previously-presented features: label space partitioning based on sensor fields of view, the ensemble Gaussian mixture filter (EnGMF), and bi-fidelity orbit uncertainty propagation. We also introduce a new, algorithmically simple method for adjusting the number of EnGMF particles to balance accuracy and computational efficiency, which we refer to as progressive resampling.