Generalized multiple model adaptive attitude estimation without rate gyros
Marschke, Jeremy M.
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A generalized multiple model adaptive estimation (GMMAE) scheme is derived to determine the attitude of a spacecraft without the use of rate information provided by gyros. Multiple model adaptive estimation (MMAE) uses several extended Kalman filters (EKFs) running in parallel, each representing a hypothesis of the actual system, to generate enhanced state and parameter estimates. The estimates of each parallel filter are combined based on the likelihood that each hypothesis is correct, which is determined from measurement residuals. GMMAE is an extension of this approach wherein a window of previous-time data is used via the autocorrelation matrix to perform the adaptive update. This approach shows significant improvement over both the standard EKF and standard MMAE approaches in the convergence of the estimates. Each of the multiple models within the filter makes a separate hypothesis as to the process noise covariance of the system, and the combination of these separate filters allows it to be adaptively estimated. The filter formulation is based on the standard equations of attitude dynamics, with global attitude parameterization given by a quaternion. A multiplicative quaternion-error approach is used to guarantee that quaternion normalization is maintained in the filters. Both Markov model and dynamic approaches are considered for modeling the angular rates. The Markov model approaches are advantageous in that they require no torque input knowledge. The dynamic approaches are advantageous in that they model the exact dynamics of the system; however this requires accurate knowledge of both the inertia properties of the spacecraft and its control torque inputs. Simulation results are provided to compare models and estimation algorithms.