Particle Filter
Particle Filter, also known as Sequential Monte Carlo (SMC) methods, is a statistical technique used for state estimation in dynamic systems, particularly in robotics, computer vision, and signal processing. It approximates the posterior distribution of a system's state using a set of weighted random samples called particles, which are updated over time based on measurements and system dynamics. This method is especially effective for non-linear and non-Gaussian systems where traditional filters like Kalman filters fail.
Developers should learn Particle Filter when working on real-time tracking, localization, or state estimation problems in fields like autonomous vehicles, robotics, and augmented reality, where systems exhibit non-linear behavior or non-Gaussian noise. It is crucial for applications such as robot localization in SLAM (Simultaneous Localization and Mapping), object tracking in video, and financial modeling, providing robust estimates in complex, uncertain environments.