Adaptive Neuro-Fuzzy Extended Kalman Filtering for Robot Localization
Extended Kalman Filter (EKF) has been a popular approach to
localization a mobile robot. However, the performance of the
EKF and the quality of the estimation depends on the correct a
priori knowledge of process and measurement noise covariance
matrices (Qk and Rk , respectively). Imprecise knowledge of
these statistics can cause significant degradation in performance.
This paper proposed the development of an Adaptive Neuro-
Fuzzy Extended Kalman Filtering (ANFEKF) for localization of
robot. The Adaptive Neuro-Fuzzy attempts to estimate the
elements of Qk and Rk matrices of the EKF algorithm, at each
sampling instant when measurement update step is carried out.
The ANFIS supervises the performance of the EKF with the aim
of reducing the mismatch between the theoretical and actual
covariance of the innovation sequences. The free parameters of
ANFIS are trained using the steepest gradient descent (SD) to
minimize the differences of the actual value of the covariance of
the residual with its theoretical value as much possible. The
simulation results show the effectiveness of the proposed
algorithm.
Keywords: Extended Kalman Filter, Localization, Fuzzy
Inference System and Neuro-Fuzzy
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