Improved Particle Filter for Non-Gaussian Forecasting-aided State Estimation

State Estimation of CSTR Using Particle Filter

In this paper, Particle Filter algorithm has been employed for estimating the states namely concentration and temperature of a Continuous Stirred Tank Reactor (CSTR) and simulation results are presented. The propagation of particles through the nonlinear system model for the state estimation has been discussed. The states of the system are estimated by using the Particle Filter algorithm under ...

The Gaussian Particle Filter for Diagnosis of Non-linear Systems

Abstract: Fault diagnosis is a critical task for autonomous operation of systems such as spacecraft and planetary rovers, and must often be performed on-board. Unfortunately, these systems frequently also have relatively little computational power to devote to diagnosis. For this reason, algorithms for these applications must be extremely efficient, and preferably anytime. In this paper we intr...

A robust particle filter for state estimation - with convergence results

Particle filters are becoming increasingly important and useful for state estimation in nonlinear systems. Many filter versions have been suggested, and several results on convergence of filter properties have been reported. However, apparently a result on the convergence of the state estimate itself has been lacking. This contribution describes a general framework for particle filters for stat...

Particle Filter with Hybrid Proposal Distribution for Nonlinear State Estimation

Particle filters have been widely used in solving nonlinear filtering problems. Proposal Distribution design is a key issue for these methods and has vital effects on simulation results. Various proposal distributions have been proposed to improve the performance of particle filters, but practical situations have encouraged the researchers to design better candidate for proposal distributions i...

An improved fuzzy Kalman filter for state estimation of non-linear systems