Matrix analysis
Tracking and data association
Adaptive fading Kalman filter with an application
Automatica (Journal of IFAC)
Unbiased minimum variance estimation for systems with unknown exogenous inputs
Automatica (Journal of IFAC)
Robust extended Kalman filtering
IEEE Transactions on Signal Processing
Robust mixed ℋ2/ℋ∞ filteringof 2-D systems
IEEE Transactions on Signal Processing
Optimal linear filtering under parameter uncertainty
IEEE Transactions on Signal Processing
Robust discrete-time minimum-variance filtering
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Finite-horizon robust Kalman filter design
IEEE Transactions on Signal Processing
WSEAS Transactions on Computers
Brief paper: Adaptive divided difference filtering for simultaneous state and parameter estimation
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
On-line Support Vector Regression of the transition model for the Kalman filter
Image and Vision Computing
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In this paper, a class of linear systems subject to process disturbances and structured measurement disturbances with unknown time-varying covariances is considered. First, we construct a finite-horizon filter structure to recursively obtain a suit of positive definite matrices and propose the sufficient conditions to ensure the above positive definite matrices to be upper bounds of the unknown covariances of the state estimation errors, filtering residuals and state prediction errors. Then some parameters are directly determined through simultaneously minimizing such upper bounds, while the other parameters are obtained via optimization through minimizing the upper bound of the covariances of filtering residuals. Furthermore, the parameter optimization is transformed into a convex optimization problem, which can be effectively solved by use of linear matrix inequality (LMI). Hence a finite-horizon adaptive Kalman filter (FHAKF) is proposed. The simulation study is about the joint time-varying time delay and parameter estimation of a nonlinear stochastic system with sensors subject to disturbances with unknown covariances, which shows that the proposed FHAKF has excellent performance and reveals the robustness of the FHAKF against the a priori filter parameters.