Optimal estimation problems for non-linear non-Gaussian state-space models do not
typically admit analytic solutions. Since their introduction in 1993, particle filtering methods …

It is now over a decade since the pioneering contribution of Gordon (1993), which is
commonly regarded as the first instance of modern sequential Monte Carlo (SMC) …

High-dimensional nonlinear state estimation is at the heart of inertial-aided navigation
systems (INS). Traditional methods usually rely on good initialization and find difficulty in …

Online reconstruction based on RGB-D sequences has thus far been restrained to relatively
slow camera motions (< 1m/s). Under very fast camera motion (eg, 3m/s), the reconstruction …

The particle filter (PF) was introduced in 1993 as a numerical approximation to the nonlinear
Bayesian filtering problem, and there is today a rather mature theory as well as a number of …

A framework for positioning, navigation, and tracking problems using particle filters
(sequential Monte Carlo methods) is developed. It consists of a class of motion models and …

In this self-contained survey/review paper, we systematically investigate the roots of
Bayesian filtering as well as its rich leaves in the literature. Stochastic filtering theory is …

The particle filter offers a general numerical tool to approximate the posterior density
function for the state in nonlinear and non-Gaussian filtering problems. While the particle …

The use of heterogeneous, non‐collocated measurements for nonlinear structural system
identification is explored herein. In particular, this paper considers the example of sensor …

Abstract The term “sequential Monte Carlo methods” or, equivalently,“particle filters,” refers
to a general class of iterative algorithms that performs Monte Carlo approximations of a …