Abstract Two Kalman-filter formulations are presented for the estimation of spacecraft sensor misalignments from inflight data. In the first the sensor misalignments are part of the filter state variable; in the second, which we call HYLIGN, the state vector contains only dynamical variables, but the sensitivities of the filter innovations to the misalignments are calculated within the Kaiman filter. This procedure permits the misalignments to be estimated in batch mode as well as a much smaller dimension for the Kaiman filter state vector. This results not only in a significantly smaller computational burden but also in a smaller sensitivity of the misalignment estimates to outliers in the data. Numerical simulations of the filter performance are presented.
Batch, Sequential and Hybrid Approaches to Spacecraft Sensor Alignment Estimation
2003
Aufsatz (Zeitschrift)
Englisch
Batch, Sequential and Hybrid Approaches to Spacecraft Sensor Alignment Estimation
Online Contents | 2003
|Batch, Sequential and Hybrid Approaches to Spacecraft Sensor Alignment Estimation
Springer Verlag | 2003
|Batch and filter approaches to spacecraft sensor alignment estimation
British Library Conference Proceedings | 1997
|Batch and filter approaches to spacecraft sensor alignment estimation
British Library Online Contents | 1997
|Spacecraft alignment estimation
Tema Archiv | 1988
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