This paper proposes an optimized algorithm for 3-D Point Based Rigid Registration. This algorithm uses an Unscented Kalman filter (UKF) for estimating the state vector of transformation, which can be interpreted as a nonlinear function of translation and rotation. In the previous work, we showed that the drawback of the UKF algorithm in estimating high range rotations is due to its sensitivity ...

The stability problem is one of the existing problems of unscented Kalman filter (UKF), which due to the nonlinearity and complexity of system. The precision will be decreased or even UKF will be halted when the algorithm can’t ensure the state covariance to be positive semidefinite. Based on decomposition of state covariance, the paper first reviews two modified UKFs which can enhance the stat...

Based on the Unscented Kalman Filter (UKF), the nonlinear filter is presented for parameter estimation in linear system with correlated noise where the unknown parameters are estimated as a part of an enlarged state vector. To avoid the computational burden in determining the state estimates when only the parameter estimates are required, a new form of UKF, where the state consists only of the ...

This paper aims to investigate several new nonlinear/non-Gaussian filters in the context of the sequential data assimilation. The unscentedKalman filter (UKF), the ensemble Kalman filter (EnKF), the sampling importance resampling particle filter (SIR-PF) and the unscented particle filter (UPF) are described in the state-space model framework in the Bayesian filtering background. We first evalua...

This paper presents a new multi-passive-sensor target tracking algorithm which yields a nonlinear state estimator called Gaussian filter based on deterministic sampling. Firstly, this state estimator employs a deterministic sample selection scheme, where a parametric density function representation of the sample points is employed to approximate the cumulative distribution function of the prior...

Process modeling by means of Gaussian-based algorithms often suffers from redundant information which usually increases the estimation computational complexity without significantly improving the estimation performance. In this article, a non-arbitrary measurement selection criterion for Gaussian-based algorithms is proposed. The measurement selection criterion is based on the determination of ...

<span lang="EN-US">This paper presents the comparison between optimized unscented Kalman filter (UKF) and extended (EKF) for sensorless direct field orientation control induction motor (DFOCIM) drive. The high performance of UKF EKF depends on accurate selection state noise covariance matrices. For this goal, multi objective function genetic algorithm is used to find optimal values main o...

The performance of the selective catalytic reduction (SCR) system has been confirmed to be distinctly affected by hydrothermal aging fault. In this paper, an observer based on Unscented-Kalman-Filter (UKF) algorithm theory is designed identify states so that SCR can more accurately and efficiently diagnosed fault-tolerant controlled. Furthermore, a model reference adaptive controller (MRAC) Lya...

The univariate non-stationary growth model (UNGM) is widely used in the verification of nonlinear filters, and unscented Kalman filter (UKF) often as reference for comparative analysis when using this to evaluate performance. However, due strong nonlinearity UNGM change properties with different parameter settings, estimation misalignment problem reasons will occur UKF filtering. To solve these...

An accurate state of charge (SOC) estimation is of great importance for the battery management systems of electric vehicles. To improve the accuracy and robustness of SOC estimation, lithium-ion battery SOC is estimated using an adaptive square root unscented Kalman filter (ASRUKF) method. The square roots of the variance matrices of the SOC and noise can be calculated directly by the ASRUKF al...