Adaptive fading Kalman filter with an application
Automatica (Journal of IFAC)
A finite-difference method for linearization in nonlinear estimation algorithms
Automatica (Journal of IFAC)
A finite-horizon adaptive Kalman filter for linear systems with unknown disturbances
Signal Processing - Signal processing in communications
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Finite-horizon robust Kalman filter design
IEEE Transactions on Signal Processing
New developments in state estimation for nonlinear systems
Automatica (Journal of IFAC)
Improved robust H2 and H∞ filtering for uncertain discrete-time systems
Automatica (Journal of IFAC)
Performance evaluation of UKF-based nonlinear filtering
Automatica (Journal of IFAC)
IEEE Transactions on Neural Networks
A Gaussian approximation recursive filter for nonlinear systems with correlated noises
Automatica (Journal of IFAC)
Engineering Applications of Artificial Intelligence
Gaussian filter for nonlinear systems with one-step randomly delayed measurements
Automatica (Journal of IFAC)
Hi-index | 22.15 |
A novel adaptive version of the divided difference filter (DDF) applicable to non-linear systems with a linear output equation is presented in this work. In order to make the filter robust to modeling errors, upper bounds on the state covariance matrix are derived. The parameters of this upper bound are then estimated using a combination of offline tuning and online optimization with a linear matrix inequality (LMI) constraint, which ensures that the predicted output error covariance is larger than the observed output error covariance. The resulting sub-optimal, high-gain filter is applied to the problem of joint state and parameter estimation. Simulation results demonstrate the superior performance of the proposed filter as compared to the standard DDF.