Kalman filtering has emerged as a pivotal tool in the field of multibody system dynamics, offering a robust framework for real-time state and parameter estimation in systems composed of interconnected ...

It appears that no particular approximate [nonlinear] filter is consistently better than any other, though . . . any nonlinear filter is better than a strictly linear one. 1 The Kalman filter is a ...

Fractional-order Kalman filtering extends traditional state estimation by incorporating fractional calculus, which enables the modelling of memory and hereditary properties in complex systems. This ...

If you program using values that represent anything in the real world, you have probably at least heard of the Kalman filter. The filter allows you to take multiple value estimates and process them ...

This section describes a collection of Kalman filtering and smoothing subroutines for time series analysis; immediately following are three examples using Kalman filtering subroutines. The state space ...

Electric vehicles (EVs) have emerged as a promising trend for future development. Serving as the core energy source for EVs, lithium-ion batteries offer advantages. Accurate SoC estimation is vital ...

(A) 3D model of the manipulator structure, consisting of 3 continuum segments. The manipulator operates in the plane. (B) Close-up view of the revolute joint between adjacent disks. (C) Diagram ...

Using Kalman equations, we derive straightforward formulas for the total imputation variance for several imputation methods commonly used in regression analysis and (un)equal probability sampling ...

The time series analysis subroutines are an adaptation of parts of the TIMSAC (TIMe Series Analysis and Control) package developed by the Institute of Statistical Mathematics (ISM) in Japan.