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A decentralized estimation architecture for large formations of spacecraft is introduced that, when coupled with a local controller, parameterizes the degree to which a node is a leader or a follower, eliminating the rigid classification of nodes as strictly leaders and followers. Measurements are provided by a single range/bearing sensor similar to the autonomous formation flying sensor. A reference point calculation is introduced that automatically compensates for noise in the estimation and sensing subsystems. A scheduling algorithm is developed that maximizes the information collected across the formation. The scheduling problem is posed as an infinite horizon optimization of either the formation information or covariance matrices, or the reference point information or covariance matrices. The resulting architecture is compared via simulation to a system in which each spacecraft has full knowledge of the fleet. Results show that performance degradation (root mean square position error and fuel usage) is very small compared to the ideal, full knowledge solution, and that the system performs better than the traditional leader-follower architecture.Nomenclature C i = measurement function Jacobian at spacecraft i G i = free virtual center weighting matrix at spacecraft i g i = free scalar virtual center weight at spacecraft i J 1 = infinite horizon measurement scheduling cost K = measurement period (dimensionless) N = number of spacecraft Q = system process noise covariance relative to spacecraft i v i = collection of measurement noise at spacecraft i w i = system process noise disturbance relative to spacecraft i x ci = system virtual center state relative to spacecraft î x ci = virtual center state estimate at spacecraft i x i = system state relative to spacecraft î x i = system state estimate at spacecraft i Y ci = information matrix of the virtual center state estimate at spacecraft i Y i = information matrix of the system state estimate at spacecraft i Y p = nominal steady-state periodic information matrix z i = collection of system measurements at spacecraft i j = measurement duty to spacecraft j K = periodic measurement sequence 1 = infinite horizon measurement schedule

This paper describes several tools and technologies that have been developed for future spacecraft formation flying missions. This includes algorithms to perform autonomous navigation and control, with new results presented for trajectory planning in highly elliptic orbits and fault detection for a distributed spacecraft system. The overall approach is embedded in the OA middleware developed by Princeton Satellite Systems, which provides a seamless integration of networking and process control with C/C++ software development. New results in this paper also demonstrate OA integrated with software that is running in hard real-time. A new multi-computer spacecraft formation flying testbed was created to simulate the performance of the full system. In particular, simulation results of an optimally initialized "recurring tetrahedron formation" on a highly elliptical orbit are shown to demonstrate both the functionality of the new testbed and the potential for creating fuel-efficient formation configurations relevant to future magnetospheric space science.

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