Kalman Filtering and Neural Networks
Kalman Filtering and Neural Networks
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Brief Design and analysis of discrete-time robust Kalman filters
Automatica (Journal of IFAC)
Adaptive Gating for Multitarget Tracking With Gaussian Mixture Filters
IEEE Transactions on Signal Processing
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In this paper, nonlinear state estimation problems with modeling uncertainties are considered. As demonstrated recently in literature, the cubature Kalman filter (CKF) provides the closest known approximation to the Bayesian filter in the sense of preserving second-order information contained in noisy measurements under the Gaussian assumption. The smooth variable structure filter (SVSF) has also been recently introduced and has been shown to be robust to modeling uncertainties. In an effort to utilize the accuracy of the CKF and the robustness of the SVSF, the CKF and SVSF have been combined resulting in an algorithm referred to as the CK-SVSF. The robustness and accuracy of the CK-SVSF was validated by testing it on two different computer problems, namely, a target tracking problem and the estimation of the effective bulk modulus in an electrohydrostatic actuator.