IEKFIndo-European Kashmir Forum (London, UK)
IEKFIterated Extended Kalman Filter
IEKFInvariant Extended Kalman Filter (systems possessing)
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References in periodicals archive ?
In the proposed optimal IEKF and IPF algorithms, each particle performs to update on the observed landmarks as a simple mapping operation with known mobile robot pose.
Compared with the EKF, the IEKF employs a few simple iterative operations to reduce the bias and the estimation error after getting [X.sub.k] in (2) and [P.sub.k] in (3).
The position errors for the INS only, WSN, EKF, IEKF, and the proposed method are shown in Figure 5.
It is evident that both the EKF and the IEKF are effective in reducing the position error compared with WSN.
It reduces the mean RMSE of position by about 92.53%, 67.93%, 55.97%, and 30.09% compared with the INS only, WSN, EKF, and IEKF.
It can be seen that the EKF, IEKF, and the proposed method are able to reduce the velocity error compared with the IN S and the WSN, respectively.