An unscented transformation approach to stochastic analysis of measurement uncertainty in magnet resonance imaging with applications in engineering
In the frame of stochastic filtering for nonlinear (discrete-time) dynamic systems, the unscented transformation plays a vital role in predicting state information from one time step to another and correcting apriori knowledge of uncertain state estimates by available measured data corrupted by rand...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2021-03-01
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| Series: | International Journal of Applied Mathematics and Computer Science |
| Subjects: | |
| Online Access: | https://doi.org/10.34768/amcs-2021-0006 |