Multi-Target Tracking using Random Finite Sets for Rendezvous and Proximity Operations with Non-Gaussian Uncertainties

This program will develop an innovative Random Finite Set (RFS)-theory-based software tool for Multi-Target Tracking (MTT), using measurement filtering methods that include the Sequential Monte Carlo Generalized Labeled Multi-Bernoulli (SMC-GLMB) and the Studentrsquo;s t-Mixture GMLB (STM-GLMB) filters. These MTT methods enable classification and tracking of objects within the field of view of spacecraft, including a target spacecraft for rendezvous, secondary spacecraft, orbital debris, or other planetary bodies. In this program, ASTER Labsrsquo; team will develop RFS-based algorithms that will improve the reliability of sensor measurement gathering, object classification, and target tracking, even in the presence of high levels of non-Gaussian noise. The newly developed RFS-MTT Toolset will integrate RFS-based algorithms with Clohessy-Wiltshire-Hill, Tschauner-Hempel, and Karlgaard relative orbital dynamics equations, sensor and uncertainty models, and non-Gaussian noise-generation methods to form a full software package for simulation and analytical purposes. Orbital trajectory data from databases maintained by NORAD that feature multiple rendezvous maneuvers will be utilized along with noise models to create additional measurement uncertainty. This data will be processed via the developed RFS-MTT Toolset to confirm fidelity of the dynamics models, analyze the RFS-based algorithms, and verify the algorithmsrsquo; ability to accurately track targets in high-clutter and high sensor noise environments. Phase I will focus on developing the RFS-MTT Toolset and associated algorithms for simulations and performance assessment in orbital spacecraft rendezvous and proximity operations. The project will also evaluate these algorithms for eventual incorporation into NASArsquo;s existing software tools, e.g. GEONS.nbsp;