A real-time recursive filter for the attitude determination of the Spacelab instrument pointing subsystem

Details A real-time recursive filter for the attitude determination of the Spacelab instrument pointing subsystem A real-time recursive filter for the attitude determination of the Spacelab instrument pointing subsystem

Feb 1992

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1992stin...9219920w&link_type=abstract

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Mathematics

Attitude Control, Error Analysis, Linear Filters, Nonlinear Filters, Real Time Operation, Spacelab, Three Axis Stabilization, Kalman Filters, Nonlinear Systems, Robustness (Mathematics), Stabilized Platforms, State Estimation

Scientific paper

A real-time estimation filter which reduces sensitivity to system variations and reduces the amount of preflight computation is developed for the instrument pointing subsystem (IPS). The IPS is a three-axis stabilized platform developed to point various astronomical observation instruments aboard the shuttle. Currently, the IPS utilizes a linearized Kalman filter (LKF), with premission defined gains, to compensate for system drifts and accumulated attitude errors. Since the a priori gains are generated for an expected system, variations result in a suboptimal estimation process. This report compares the performance of three real-time estimation filters with the current LKF implementation. An extended Kalman filter and a second-order Kalman filter are developed to account for the system nonlinearities, while a linear Kalman filter implementation assumes that the nonlinearities are negligible. The performance of each of the four estimation filters are compared with respect to accuracy, stability, settling time, robustness, and computational requirements. It is shown, that for the current IPS pointing requirements, the linear Kalman filter provides improved robustness over the LKF with less computational requirements than the two real-time nonlinear estimation filters.

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