Kalman Filter With Dynamical Setting of Optimal Process Noise Covariance
We propose a dynamical way to set the process error covariance matrix (Q) for a constant velocity (CV) model Kalman filter. We are able to achieve the best possible solution for the estimated state, in the sense of forecast error, while significantly reducing the convergence time at no significant c...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2017-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/7914658/ |