A Particle Filter is a statistical method used for estimating the state of a system over time, particularly when the system is subject to uncertainty. It represents the state with a set of random samples, called particles, which are weighted based on how well they match the observed data. This approach is useful in various fields, including robotics and computer vision, where tracking and predicting the position of objects is essential. The process involves two main steps: prediction and update. In the prediction step, particles are moved according to a model of the system's dynamics, while in the update step, the weights of the particles are adjusted based on new observations. This iterative process allows the filter to provide a more accurate estimate of the system's state as new data becomes available.