# BLUE, BLUP and the Kalman filter: some new results

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## Abstract

In this contribution, we extend ‘Kalman-filter’ theory by introducing a new BLUE–BLUP recursion of the partitioned measurement and dynamic models. Instead of working with known state-vector means, we relax the model and assume these means to be unknown. The recursive BLUP is derived from first principles, in which a prominent role is played by the model’s misclosures. As a consequence of the mean state-vector relaxing assumption, the recursion does away with the usual need of having to specify the initial state-vector variance matrix. Next to the recursive BLUP, we introduce, for the same model, the recursive BLUE. This extension is another consequence of assuming the state-vector means unknown. In the standard Kalman filter set-up with known state-vector means, such difference between estimation and prediction does not occur. It is shown how the two intertwined recursions can be combined into one general BLUE–BLUP recursion, the outputs of which produce for every epoch, in parallel, the BLUP for the random state-vector and the BLUE for the mean of the state-vector.

## Keywords

Best linear unbiased estimation (BLUE) Best linear unbiased prediction (BLUP) Minimum mean squared error (MMSE) Misclosures Kalman filter BLUE–BLUP recursion## Notes

### Acknowledgments

P.J.G. Teunissen is the recipient of an Australian Research Council Federation Fellowship (project number FF0883188). The research of A. Khodabandeh was carried out whilst a Curtin International Research Scholar at Curtin’s GNSS Research Centre. All this support is gratefully acknowledged.

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