Decentralized Estimation Algorithms for Formation Flying Spacecraft

Several space missions are being planned with large fleets of spacecraft at GEO and beyond where relative navigation using GPS will either be impossible or insufficient. Onboard local ranging devices will be required to replace or augment the available GPS measurements to perform relative navigation for these missions. The vast amounts of distributed data and the computational load limitations suggest that these navigation algorithms should be highly distributed, but the nonlinear local range measurements couple the states of the vehicles, which greatly complicates this decentralization. This paper investigates several estimator architectures and the associated algorithms for determining the fleet state, and then compares the resulting performance, computation, and communication requirements. This analysis shows that the proposed decentralized reducedorder filters provide near optimal estimation results without excessive communication or computation. Embedding these reduced-order estimators within the hierarchic architecture presented should permit scaling of the relative navigation to very large fleets.