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. This thesis presents an attempt to enable real-time particle filtering for complex estimation problems using modern GPU hardware. We propose a GPU-based generic particle filtering framework which can be applied to various estimation problems. We implement a real-time estimation application using this particle filtering framework and measure the estimation error with different filter parameters. Furthermore, we present an in-depth performance analysis of our GPU implementation followed by a number of optimisations in order to increase implementation efficiency.