Filtering is a recursive estimation of hidden states of a dynamic system from noisy
measurements. Such problems appear in several branches of science and technology …

Nonlinear Estimation: Methods and Applications with Deterministic Sample Points focusses
on a comprehensive treatment of deterministic sample point filters (also called Gaussian …

Motion artifact (MA) contamination with electrocardiogram (ECG) signal is a common issue
caused by body movement or sensor loosening, resulting in distortion of clinical features of …

The inverse problem of estimating time-invariant (static) parameters of a nonlinear system
exhibiting noisy oscillation is considered in this paper. Firstly, a Markov Chain Monte Carlo …

The popularly known Gaussian filtering witnesses intractable integrals numerically
approximated during the filtering. However, the numerical approximation methods used in …

The problem of identifying parameters of nonlinear vibrating systems using spatially
incomplete, noisy, time-domain measurements is considered. The problem is formulated …

The paper proposes a strategy for combining two powerful computational procedures,
namely, the finite element method (FEM) for structural analysis and particle filtering for …

This paper examines and contrasts the feasibility of joint state and parameter estimation of
noise-driven chaotic systems using the extended Kalman filter (EKF), ensemble Kalman filter …

The problem of identification of parameters of a beam-moving oscillator system based on
measurement of time histories of beam strains and displacements is considered. The …

Generally, memoryless polynomial nonlinear model for nonlinear part and finite impulse
response (FIR) model or infinite impulse response model for linear part are preferred in …