Number Of Sigma Points Research Articles

Implementation of the unscented transformation with low rank approximation in uncertainty analysis during large-break loss of coolant accident

The Low Rank Approximation (LRA) and Unscented Transform (UT) are integrated to produce a new algorithm having the capability to decrease the time required for the uncertainty quantification during Loss of coolant accident (LOCA) in Pressurized Water Reactors (PWR). The LRA is an efficient technique used in reducing computational cost due to its ability to perform dimensionality reduction by revealing the active or important degrees of freedom and calculate the basis of the so-called active subspace basing on the Singular Value Decomposition (SVD). For further reduction in the computational time; the UT algorithm is also implemented to generate a set of sigma points, these sigma points are the representatives of the whole probability distribution (the UT is restricted to Gaussian distribution). The main safety parameter is the maximum cladding temperatures during the accident which are computed by ATHLET thermal-hydraulic code. The reactivity coefficients and the covariance matrix are calculated using the SCALE 6.2 code. The present calculation model has 14-dimensions, therefore the number of sigma points needed for the SVD/UT technique is 29, and can be minimized to 5 sigma points only if the LRA/UT is used where two singular values are sufficient to reproduce/span the space thanks to the strong correlations between the reactivity coefficients.

An Improved Unscented Kalman Filter for Discrete Nonlinear Systems with Random Parameters

This paper investigates the nonlinear unscented Kalman filtering (UKF) problem for discrete nonlinear dynamic systems with random parameters. We develop an improved unscented transformation by incorporating the random parameters into the state vector to enlarge the number of sigma points. The theoretical analysis reveals that the approximated mean and covariance via the improved unscented transformation match the true values correctly up to the third order of Taylor series expansion. Based on the improved unscented transformation, an improved UKF method is proposed to expand the application of the UKF for nonlinear systems with random parameters. An application to the mobile source localization with time difference of arrival (TDOA) measurements and sensor position uncertainties is provided where the simulation results illustrate that the improved UKF method leads to a superior performance in comparison with the normal UKF method.

Marginalized Unscented Quaternion Estimator for Integrated INS/GPS

The UnScented QUaternion Estimator (USQUE) has been approved as a promising substitute for the extended Kalman filter when applied in the Standard Inertial Navigation Equations (SINE)-based inertial navigation system/global positioning system integration. However, the expensive computational burden makes it untenable in real time applications. In this paper, a computationally efficient filtering algorithm called Marginalised USQUE (MUSQUE) is derived by embedding the Marginalised Unscented Transformation (MUT) that is applicable to nonlinear systems with a linear substructure into the widely used USQUE. The contributions of the MUSQUE developed here are twofold. Firstly, the SINE are reconstructed to be only nonlinear in the attitude quaternion while linear in velocity and position, which makes the MUT potentially applicable in the USQUE. Secondly, the quaternion and generalised Rodrigues parameter-based sigma points are propagated simultaneously in the MUSQUE to deal with the dimensional mismatching hierarchy of the USQUE. Compared with the USQUE the number of sigma points can be decreased substantially, thereby making the applied MUSQUE computationally tenable. The experimental results show that the proposed MUSQUE has nearly identical performance with the USQUE but with much reduced computational burden.

State estimation of nonlinear dynamical systems using nonlinear update based Unscented Gaussian Sum Filter

Two attractive features of Unscented Kalman Filter (UKF) are: (1) use of deterministically chosen points (called sigma points), and (2) only a linear dependence of the number of sigma points on the number of states. However, an implicit assumption in UKF is that the prior conditional state probability density and the state and measurement noise densities are Gaussian. To avoid the restrictive Gaussianity assumption, Gaussian Sum-UKF (GS-UKF) has been proposed in literature that approximates all the underlying densities using a sum of Gaussians. However, the number of sigma points required in this approach is significantly higher than in UKF, thereby making GS-UKF computationally intensive. In this work, we propose an alternate approach, labeled Unscented Gaussian Sum Filter (UGSF), for state estimation of nonlinear dynamical systems, corrupted by Gaussian state and measurement noises. Our approach uses a Sum of Gaussians to approximate the non-Gaussian prior density. A key feature of this approximation is that it is based on the same number of sigma points as used in UKF, thereby resulting in similar computational complexity as UKF. We implement the proposed approach on two nonlinear state estimation case studies and demonstrate its utility by comparing its performance with UKF and GS-UKF.

Simplified Gauss Hermite Filter Based on Sparse Grid Gauss Hermite Quadrature

In order to improve estimation accuracy of nonliear system with linear measurement model, simplifed gauss hermite filter based on sparse grid gauss hermite quadrature (SGHF) is proposed. Comparing to conventional Gauss-Hermite filter (GHF) based on tensor product gauss quadrature rule, simplified SGHF not only maintains GHF's advantage of precission controllable, high estimation accuracy, but also relieves the curse of dimension problem by reduce the number of gaussian intrgration points to the number of sigma points that scaled unscented transform uses. Theoretical analysis and experimental results show that estimation base on new filter performs significantly beter than extended kalman filter (EKF), and slightly better than unscented kalman filter (UKF) on estimation accuracy and convergence speed, and computational burden is significantly reduced comparing with traditional GHKF. DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.3693 Full Text: PDF

Research on Non-Gyro Projectile’s Attitude Measurement Based on Improved Unscented Kalman Filter

Considering the control requirements of guided projectile, a novel method is studied that using three low-cost MEMS accelerometers as inertial measurement unit to construct state equation and measurement equation of system, using improved unscented Kalman filter (IUKF) to estimate the state of system. For the system characteristic of linear state equation and nonlinear measurement equation, the improved UKF nonlinear filter algorithm which combines KF and UKF was proposed. At the same time, utilizing minimal skew simplex sampling to reduce the number of sigma points, computational efficiency is enhanced. The simulation experimental results show that using IUKF algorithm can obtains good precision of estimation.