A formation estimation architecture for formation flying builds upon the local information exchange among multiple local estimators. Spacecraft formation flying involves the coordination of states among multiple spacecraft through relative sensing, inter-spacecraft communication, and control. Most existing formation flying estimation algorithms can only be supported via highly centralized, all-toall, static relative sensing. New algorithms are needed that are scalable, modular, and robust to variations in the topology and link characteristics of the formation exchange network. These distributed algorithms should rely on a local information- exchange network, relaxing the assumptions on existing algorithms.
In this research, it was shown that only
local observability is required to design a
formation estimator and control law.
The approach relies on breaking up the
overall information-exchange network
into sequence of local subnetworks, and
invoking an agreement-type filter to
reach consensus among local estimators
within each local network. State estimates
were obtained by a set of local
measurements that were passed through
a set of communicating Kalman filters to
reach an overall state estimation for the
An optimization approach was also presented by means of which diffused estimates over the network can be incorporated in the local estimates obtained by each estimator via local measurements. This approach compares favorably with that obtained by a centralized Kalman filter, which requires complete knowledge of the raw measurement available to each estimator.