The real-world applications in signal processing generally involve estimating the system
state or parameters in nonlinear, non-Gaussian dynamic systems. The estimation problem …

This paper investigates constrained Bayesian state estimation problems by using a Particle
Filter (PF) approach. Constrained systems with nonlinear model and non-Gaussian …

Moving horizon estimation (MHE) is a numerical optimization based approach to state
estimation, where the joint probability density function (pdf) of a finite state trajectory is …

State estimation from plant measurements plays an important role in advanced monitoring
and control technologies, especially for chemical processes with nonlinear dynamics and …

Increasingly in practical applications, nonlinearity, non‐Gaussianity, and constraint must be
considered to obtain good state estimation. A constrained particle filter (PF) approach for …

Recursive state estimation of constrained nonlinear dynamical system has attracted the
attention of many researchers in recent years. For nonlinear/non-Gaussian state estimation …

Constraints on the state vector must be taken into account in the state estimation problem.
Recently, acceptance/rejection and projection methods are proposed in the particle filter …

Increasingly in practical applications, nonlinearity, non-Gaussianity, and constraint are
considered when dealing with state estimation problems. This paper proposes a novel …

Chemical engineering processes need careful monitoring in order to ensure product
specification (ie, composition, quality, etc.). Physicochemical analytical techniques can be …

Nonlinear estimation techniques play an important role for process monitoring since some
states and most of the parameters cannot be directly measured. There are many techniques …