Distributed Unscented-Information Kalman Filter (UIKF) for Cooperative Localization in Spacecraft Formation Flying
Hermann Kaptui Sipowa, Jay W. McMahon, Taralicin Deka · AIAA Scitech 2020 Forum · 2020
In this work, we proposed two algorithms for cooperative localization for spacecraft formation applications, namely an Unscented Information Filter (UIKF) added Consensus Averaging (CA) and an Unscented Information Filter added Covariance Intersection (CI). The UIKF was used because it operates on the information, and it is suited for nonlinear mapping. The CA is an information fusion algorithm that allows the distributed estimate across a sensors network. We proposed convergence criteria that would enable the distributed estimation generated by the CA algorithm to be equivalent to that of a central fusion node. The CI algorithm allows for producing a consistent estimate when the cross-correlation between the different nodes is unknown. The proposed algorithms are used to estimate the motion of a spacecraft flying in formation around a circular chief.