… information of a random bias is known. But the information of a random bias is unknown
or … adaptive filters, such as an adaptive fading Kalman filter (AFKF) and an adaptive two-stage …

… be random constant biases. In this case, a standard technique is to augment the Kalman
filter state vector and estimate the random biases. In an attempt to decouple the bias estimation …

… In order to keep the Kalman filter optimal in the presence of unknown bias, it is necessary
that the error component of the model is only a random variation. The ColKF and the SepKF …

… Kalman filter for state filtering in the presence of random bias. The proposed two-stage filter
includes two-reduced order filters which optimally implement the augmented Kalman filter. …

… presence of unknown constant bias [7], [8] or a random bias [9]-[13] were suggested as the
two-stage Kalman filter (… the filter for nonlinear systems in the presence of random bias based …

… bias. We evaluate the effectiveness of the proposed augmented state ensemble Kalman filter
… We recall that since the error terms are modeled as random vectors, the parameters to be …

… occurred because of the presence of model bias, and many … of random error corrections
(for a regular Kalman filter without bias removal) also introduces inaccurate propagation of bias …

… of the fullorder separate-bias Kalman filter, we introduce the … where ~x2 is the estimate of x2
if no bias were present. The … the presence of random bias,” IEEE Trans. Automat. Contr., vol. …

… We create the initial bias ensemble by randomly sampling from a Gaussian distribution with
mean zero and a standard deviation based on the desired initial spread of the ensemble. We …

… with zero-bias, constant-bias, and random-bias simultaneously. Moreover, the Kalman filter
used as … A detection rule is developed to indicate the presence of biases and select the valid …