Results 91 to 100 of about 19,010 (211)
IET Generation, Transmission &Distribution, Volume 20, Issue 1, January/December 2026.This paper proposes a time‐varying inertia estimation framework based on sensitivity‐guided clustering and aggregation. It can achieve high‐accuracy inertia estimation with limited measurements, reducing the relative error by nearly 10% compared with existing methods, and exhibit strong robustness to noise and disturbances.
Yulong Li +6 more
wiley +1 more source
Comparisons on Kalman-Filter-Based Dynamic State Estimation Algorithms of Power Systems
IEEE Access, 2020 The Kalman-filter-based algorithms as the mainstream algorithms of dynamic state estimation of power systems have been extensively used to provide accurate data for power system applications. However, few comparisons are made to show their advantages and
Hui Liu +4 more
doaj +1 more source
Mixture truncated unscented Kalman filtering [PDF]
, 2012 This paper proposes a computationally efficient nonlinear filter that approximates the posterior probability density function (PDF) as a Gaussian mixture. The novelty of this filter lies in the update step. If the likelihood has a bounded support made up of different regions, we can use a modified prior PDF, which is a mixture, that meets Bayes' rule ...
García-Fernández, AF +2 more
openaire
Unscented Kalman filter for SINS alignment
Journal of Systems Engineering and Electronics, 2007 In order to improve the filter accuracy for the nonlinear error model of strapdown inertial navigation system (SINS) alignment, Unscented Kalman Filter (UKF) is presented for simulation with stationary base and moving base of SINS alignment. Simulation results show the superior performance of this approach when compared with classical suboptimal ...
Zhou Zhanxin, Gao Yanan, Chen Liabin
openaire +1 more source
, 2018 Kalman filters and observers are two main classes of dynamic state estimation (DSE) routines. Power system DSE has been implemented by various Kalman filters, such as the extended Kalman filter (EKF) and the unscented Kalman filter (UKF).
Qi, Junjian +2 more
core
Journal of Aerospace Technology and Management, 2016 The non-linear estimators are certainly the most important algorithms applied to real problems, especially those involving the attitude estimation of spacecraft.
Roberta Veloso Garcia +2 more
doaj
Comparison of Nonlinear Filtering Techniques for Lunar Surface Roving Navigation [PDF]
Leading up to the Apollo missions the Extended Kalman Filter, a modified version of the Kalman Filter, was developed to estimate the state of a nonlinear system.
Kimber, Lemon, Welch, Bryan W.
core +1 more source
Научный вестник МГТУ ГА, 2016 The paper contains algorithms for solving the problem of nonlinear filtering. The nonlinear approximate filters presented are: the extended Kalman filter (EKF), the uncented Kalman filter (UKF) and unscented Particle Filter (UPF). The flow-charts for the listed algorithms are presented and a corresponding software complex based on these algorithms is ...
openaire +1 more source
A Bayesian Filtering Algorithm for Gaussian Mixture Models
, 2017 A Bayesian filtering algorithm is developed for a class of state-space systems that can be modelled via Gaussian mixtures. In general, the exact solution to this filtering problem involves an exponential growth in the number of mixture terms and this is ...
Hendriks, Johannes +3 more
core
Challenges with bearings only tracking for missile guidance systems and how to cope with them. [PDF]
, 2010 This paper addresses the problem of closed loop missile guidance using bearings and target angular extent information. Comparison is performed between particle filtering methods and derivative free methods.
Mihaylova, Lyudmila +2 more
core