Distributed Dual-Quaternion Extended Kalman Filtering for Spacecraft Pose Estimation

In this paper, a distributed dual-quaternion multiplicative extended Kalman filter for the estimation of poses and velocities of individual satellites in a fleet of spacecraft is analyzed. The proposed algorithm uses both absolute and relative pose measurements between neighboring satellites in a network, allowing each individual satellite to estimate its own pose and that of its neighbors. By utilizing the distributed Kalman consensus filter, a novel sensor and state-estimate fusion procedure is proposed that allows each satellite to improve its own state estimate by sharing data with its neighbors over a communication link. A leader–follower approach, whereby only a subset of the satellites has access to an absolute pose measurement, is also examined. In this case, followers rely solely on the information provided by their neighbors, as well as relative pose measurements to those neighbors. The algorithm is tested extensively via numerical simulations, and it is shown that the approach provides a substantial improvement in performance over the scenario in which the satellites do not cooperate. A case study of satellites swarming an asteroid is presented, and the performance in the leader–follower scenario is also analyzed.