Particle filter is a statistical method used for estimating the state of a system by filtering noisy and uncertain observations. It involves representing the distribution of the system state using a weighted set of samples or particles, which are propagated through time using a probabilistic model of the system dynamics. The method is commonly used in robotics, tracking, control, and other applications where state estimation is critical. Building particle filters requires expertise in mathematics, statistics, and programming.