The extended Kalman filter (EKF) is the workhorse of real-time spacecraft attitude estimation. Since the group SO3 of rotation matrices has dimension three, most attitude determination EKFs use lower- dimensional attitude parameterizations than the nine-parameter attitude matrix itself. The fact that all three- parameter representations of SO3 are singular or discontinuous for certain attitudes has led to extended discussions of constraints and attitude representations in EKFs. The most successful EKF uses a nonsingular parameterization for the global attitude, which necessarily has more than three parameters, while employing a three-component representation for the attitude errors. This filter has become known as the Multiplicative Extended Kalman Filter. These issues are now well understood, however, and the EKF has performed admirably in the vast majority of attitude determination applications. Nevertheless, poor performance or even divergence arising from the linearization implicit in the EKF has led to the development of nonlinear filters, most recently sigma point or unscented filters and particle filters.
Nonlinear Attitude Filtering Methods
AIAA Guidance, Navigation and Control Conference ; 2005 ; San Francisco, CA, United States
2005-01-04
Conference paper
No indication
English
Nonlinear Attitude Filtering Methods
AIAA | 2005
|Nonlinear filtering for spacecraft attitude estimation
AIAA | 1986
|Attitude Determination Via Nonlinear Filtering
IEEE | 1969
|Extended Kalman Filtering and Nonlinear Predictive Filtering for Spacecraft Attitude Determination
Online Contents | 2002
|Federated nonlinear predictive filtering for the gyroless attitude determination system
Online Contents | 2016
|