It is possible to implement importance sampling, and particle filter algorithms, where the
importance sampling weight is random. Such random-weight algorithms have been shown …

The likelihood calculation of a vast number of particles forms the computational bottleneck
for the particle filter in applications where the observation model is complicated, especially …

State estimation of random dynamical systems with noisy observations has been an
important problem in many areas of science and engineering. Efficient new algorithms to …

The use of stochastic differential equations (SDEs) for the simulation of aquatic ecosystems
has attracted increasing attention in recent years. The SDE setting also provides the …

For the problem of vehicle driving state and parameters estimation in the process of vehicle
driving, the vehicle state and parameters estimation algorithms are studied based on the …

While existing mathematical descriptions can accurately account for phenomena at
microscopic scales (eg, molecular dynamics), these are often high-dimensional, stochastic …

This paper presents a positioning method of the laser guidance with a particle filter for an
AGV (automatic guided vehicle). Wired guidance and magnetic guidance system have been …

We consider partially observed multiscale diffusion models that are specified up to an
unknown vector parameter. We establish for a very general class of test functions that the …

This thesis deals with stochastic dynamical systems in discrete and continuous time.
Traditionally dynamical systems in continuous time are modelled using Ordinary Differential …

The purpose of nonlinear filtering is to extract useful information from noisy sensor data. It
finds applications in all disciplines of science and engineering, including tracking and …