Particle filters are a statistical method used for estimating the state of a system over time, especially when the system is subject to noise and uncertainty. They work by representing the possible states of the system with a set of random samples, called particles. Each particle has a weight that reflects how well it matches the observed data, allowing the filter to focus on the most likely states. As new observations are made, the particle filter updates the particles and their weights through a process called resampling. This approach is particularly useful in fields like robotics and computer vision, where tracking and predicting the position of objects is essential.