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In the Extended Kalman Filter I saw that during the filtering process, the entries in the Kalman Gain matrix corresponding to the inactive DoFs are fixed to zero.
# Zero out the Kalman gain entries for the non-active DoFs. Since we aren't considering them we don't want them to affect the update process.
kalman_gain[:, nonactive_dofs] =0.0;
However, this is not being done in the Kalman Filter where the active_dofs argument is used only in the align_observations function and the entries for the inactive DoFs in the Kalman Gain matrix are not fixed to zero.
While using the KF, I did not find it giving any issues though, and I haven't tried the EKF yet. I would think that since the measurement noise is set very high for the inactive DoFs, their values would already be low enough in the Kalman Gain matrix to be negligible in such a case, which could be why they work fine in the KF without zeroing them out.
The text was updated successfully, but these errors were encountered:
In the Extended Kalman Filter I saw that during the filtering process, the entries in the Kalman Gain matrix corresponding to the inactive DoFs are fixed to zero.
intprim/intprim/filter/spatiotemporal/ekf.py
Lines 162 to 163 in 8994b15
However, this is not being done in the Kalman Filter where the
active_dofs
argument is used only in thealign_observations
function and the entries for the inactive DoFs in the Kalman Gain matrix are not fixed to zero.While using the KF, I did not find it giving any issues though, and I haven't tried the EKF yet. I would think that since the measurement noise is set very high for the inactive DoFs, their values would already be low enough in the Kalman Gain matrix to be negligible in such a case, which could be why they work fine in the KF without zeroing them out.
The text was updated successfully, but these errors were encountered: