Analysis of Decentralized Estimation Filters for Formation Flying Spacecraft

Future formation flying missions are being planned for fleets of spacecraft in MEO, GEO, and beyond where relative navigation using GPS will either be impossible or insucient. To perform fleet estimation for these scenarios, local ranging devices on each vehicle are being considered to replace or augment the available GPS measurements. A previous paper presented several approaches for distributing the computational load of the estimation process across the vehicles in the fleet. This paper extends the previous work to present a more detailed investigation of the covariances for the dierent distributed estimation algorithms. This enables an analysis of the transient eects as the ranging measurements are added to the estimator, which extends the previous steady-state comparison. This transient covariance analysis more clearly shows the eect of ignoring errors in the ranging measurements that result when the location of the target vehicle is also not well known. These results provide further insight on the relative performance of these filters and identifies the Schmidt covariance correction (SCC), the essence of the Schmidt Kalman Filter, as being responsible for better performance of Schmidt Kalman Filter compared to other decentralized filters.