Model Error Compensation Techniques for Linear Filtering
TEXAS UNIV AT AUSTIN APPLIED MECHANICS RESEARCH LAB
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The exceptional utility and performance of the sequential, linear, unbiased, minimum variance estimator suffers severely in the presence of dynamic model errors. This problem--perhaps the greatest detriment to the so-called Kalman filter algorithm--is discussed in the light of its divergent effect upon the estimation process. A number of optimal and suboptimal modifying techniques are described which attempt to prevent this divergence. Extensions are developed resulting in adaptive forms and a new algorithm is derived for sequentially estimating the state noise covariance matrix. Performance of the techniques is illustrated by their application to, 1 the terminal phase of an Earth orbit rendezvous mission, and 2 the heliocentric trajectory determination of a solar electric propulsion space vehicle. Numerical results indicate that the model error difficulties can be sufficiently countered, with particularly effective performance being supplemented by the sequential state noise covariance estimator.
- Numerical Mathematics
- Spacecraft Trajectories and Reentry