Abstract
In this contribution, we introduce a generalized Kalman filter with precision in recursive form when the stochastic model is misspecified. The filter allows for a relaxed dynamic model in which not all state vector elements are connected in time. The filter is equipped with a recursion of the actual error-variance matrices so as to provide an easy-to-use tool for the efficient and rigorous precision analysis of the filter in case the underlying stochastic model is misspecified. Different mechanizations of the filter are presented, including a generalization of the concept of predicted residuals as needed for the recursive quality control of the filter.
Original language | English |
---|---|
Article number | 108 |
Number of pages | 12 |
Journal | Journal of Geodesy |
Volume | 95 |
Issue number | 9 |
DOIs | |
Publication status | Published - 2021 |
Keywords
- Error-variance matrices
- Generalized filter
- Kalman filter
- Minimal detectable bias (MDB)
- Predicted residual
- Stochastic model