… algorithm to estimate process noise covariance matrix in real … A new Kalman filter with
recursive covariance estimation (… that the covariance matrix of process noise in Kalman filter is …

… a novel adaptive Kalman filter (KF) that can solve the filtering … (MSAKF) can adaptively estimate
the unknown parameters of … optimisation via recursive covariance estimation is introduced…

… The Kalman filter instead recursively conditions the current … covariance is referred to as an
extended Kalman filter or EKF … -1 project the state and covariance estimates from time step k to …

… covariance matrix is introduced. The factor calculation is either empirical or based on the filter
… exact models, and scaling the state error covariance matrix is the main aim of this paper in …

… Kalman filter algorithm is proposed to cope with the unknown a priori covariance matrix
of … The new solution of adaptive Kalman filter with recursive covariance estimation(NKF-RCE) …

… algorithm to estimate process noise covariance matrix in real … A new Kalman filter with
recursive covariance estimation (… that the covariance matrix of process noise in Kalman filter is …

… the case of standard Kalman filter, we assume that the observation noise variance parameters
2 … We denote the diagonal covariance matrix comprising of these variances by 6k = diag( 2 …

… The covariance estimator ˆQk or ˆRk may be likely to lose its non-… its recursive procedure
in practice. Thus, we introduce an innovation covariance estimator to depress the estimation …

… covariance matrices Qk−1, Rk might change with each time step or measurement. As the
Kalman filter is recursive estimator, … recursive filters, like Kalman and extended Kalman filters. …

… model, the KF recursively provides an optimal system state estimation in the sense … Kalman
filter with recursive covariance estimation (KFRCE) was presented in [11], which can estimate …