This thesis covers areas within estimation and optimal control of vehicles, in particular four-
wheeled vehicles. One topic is how to handle delayed and out-of-sequence measurements …

This paper addresses the out-of-sequence measurement (OOSM) problem for mixed
linear/nonlinear state-space models, which is a class of nonlinear models with a tractable …

In these notes, we introduce particle filtering as a recursive importance sampling method
that approximates the minimum-mean-square-error (MMSE) estimate of a sequence of …

We present a novel particle filtering framework for the continuous-time dynamical systems
with continuous-time measurements. Our approach is based on the duality between …

The vehicle-estimation problem is approached by fusing measurements from wheel
encoders, an inertial measurement unit, and (optionally) a global positioning system in a …

In recent years particle filters have been applied to a variety of state estimation problems. A
particle filter is a sequential Monte Carlo Bayesian estimator of the posterior density of the …

Sequential Monte Carlo methods—also known as particle filters—offer approximate
solutions to filtering problems for nonlinear state-space systems. These filtering problems …

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 …

In recent years, particle filtering has become a powerful tool for tracking signals and time-
varying parameters of random dynamic systems. These methods require a mathematical …

Standard algorithms in tracking and other state-space models assume identical and
synchronous sampling rates for the state and measurement processes. However, real …