Decentralized Relative Attitude Estimation for Three-Spacecraft Formation Flying Applications

This paper investigates the problem of relative spacecraft attitude estimation between three vehicles from a decentralized point of view. Decentralized attitude estimation is achieved through the use of local extended Kalman filters and a data fusion process known as the Covariance Intersection algorithm. Because the global attitude parameterization is the quaternion, the Covariance Intersection algorithm is modified in order to handle the quaternion norm constraint using the method of Lagrange multipliers. A multivariate Newton-Raphson iteration is developed so that state vectors containing multiple quaternions may be fused. A formation flying simulation shows that the results of state fusion via the Covariance Intersection algorithm provides substantially better results than available from any one local source.