Bayesian estimation in a bidimensional Ornstein-Uhlenbeck process

The goal of this thesis is to estimate parameters in a bidimensional Ornstein-Uhlenbeck process, namely a diffusion model which can be found in Favetto and Samson (2010), which considers plasma and interstitium concentrations. We first look at a general linear stochastic differential equation and some properties. Then we simulate possible paths...

Aircraft Mass and Thrust Estimation Using Recursive Bayesian Method

This paper focuses on estimating aircraft mass and thrust setting using a recursive Bayesian method called particle filtering. The method is based on a nonlinear state-space system derived from aircraft point-mass performance models. Using solely ADS-B and Mode-S data, flight states such as position, velocity, and wind speed are collected and...

Adapting Particle Filter Algorithms to the GPU Architecture

The particle filter is a Bayesian estimation technique based on Monte Carlo simulations. The non-parametric nature of particle filters makes them ideal for non-linear non-Gaussian systems. This greater filtering accuracy, however, comes at the price of increased computational complexity which limits their practical use for real-time applications...