The particle filter is a Bayesian estimation technique based on Monte Carlo simulation. The nonparametric nature of particle filters makes them ideal for non-linear, non-Gaussian dynamic systems. Particle filtering has many applications: in computer vision, robotics, and econometrics to name just a few. Although superior to Kalman filters, particle filters have higher computational requirements, which limits practical use in real-time applications. In this paper, we investigate how to design a particle filter framework for complex real-time estimation problems using modern many-core architectures. We develop a robotic arm application that serves as a highly flexible estimation problem to push estimation rates and accuracy to new levels. By varying different filter and model parameters, we derive rules of thumb for good filter configurations. We evaluate our particle filter with a comprehensive performance and correctness analysis. Our results significantly lower the development effort of particle filters for other real-time estimation problems. For the most demanding robotic arm configuration, we can process one million particles at an update rate of a few hundred state estimations per second. As such, we see our results as a step towards wider adoption of particle filters, and as a prerequisite to investigate larger filter setups for even more complex estimation problems.