… The two related optimal adaptive factors are given. These are analyzed and compared in …
filtering results obtained by optimal adaptive factors are superior to those obtained by adaptive …

… The Kalman filter is an observer that gives an optimal estimate x ˆ ( k ) of the state x ( k ) at
time step k given the estimate x ˆ ( k − 1 ) and the observation y ( k ) in a least squares sense, …

… Abstract-A Kalman filter requires an exact knowledge of the process noise covariance …
checks whether a particular Kalman filter is working optimally or not. If the filter is suboptimal, a …

… ], which develops an extended adaptive Kalman filter. This latter … The Kalman filter is a popular
candidate for observers of this … Kalman filter will be sub-optimal. One method of improving …

… Therefore, it is necessary to develop an adaptive filtering algorithm which has a better … to
the adaptive Kalman filtering problem, which are the multiple-model-based adaptive estimation (…

… Analysis of divergence of optimal estimators like Kalman filter (KF) [1, 5] was appeared
since the first papers given rise to the theoretical foundations of KF [4]. Indeed, it is recognized …

… To implement the identification algorithm, ten Kalman filters were … filters have the same
state-vector dimensionality as the data generation model, and filter 1 in the bank of Kalman filters …

… and [13], we propose an adaptive Kalman filter with the following properties: a) it is robust
with respect to impulsive noise, b) it provides optimum estimates of the unknown parameters, …

… Finally we present a method to adapt the Kalman filter to the … overall adaptive Kalman filter.
This technique is based on the residual characteristics of the standard optimum Kalman filter …

… a new optimal filtering, namely, Optimized Adaptive Kalman … Because Kalman filtering has
optimal accuracy in linear estimation… However the limitations of standard Kalman filtering were …