Two recursive estimation algorithms which use pairs of measured vectors to yield minimum variance estimates of the quaternion of rotation are presented. The nonlinear relations between the direction cosine matrix and the quaternion are linearized, and a variant of the extended Kalman filter is used to estimate the difference between the quaternion and its estimate. With each measurement this estimate is updated and added to the whole quaternion estimate. This operation constitutes a full state reset in the estimation process. Filter tuning is needed to obtain a converging filter. The second algorithm presented uses the normality property of the quaternion of rotation to obtain a filter which converges, with a smaller error, to a normal quaternion. Results of Monte-Carlo simulation runs are presented which demonstrate the superiority of the normalized quaternion.
Attitude determination from vector observations: quaternion estimation
Fluglagebestimmung aus Vektorbeobachtungen: Quaternionen-Abschaetzung
IEEE Transactions on Aerospace and Electronic Systems ; AES-21 , 1 ; 128-136
1985
9 Seiten, 15 Quellen
Article (Journal)
English
Quaternion Attitude Estimation Using Vector Observations
Online Contents | 2000
|Quaternion Attitude Estimation Using Vector Observations
Springer Verlag | 2000
|Quaternion Attitude Estimation Using Vector Observations
Online Contents | 2000
|