Particle Filter
A Particle Filter is a computational method used for tracking and estimating the state of a system over time, especially when the system is uncertain or noisy. It works by representing possible states (like location or position) as a collection of particles, each with a weight that reflects its likelihood of being correct. As new data comes in, the filter updates these particles, resampling them to focus on the most probable states. This approach is widely used in fields like robotics, computer vision, and finance for tasks such as navigation, predicting movements, and more.
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A particle filter is a computational method used to estimate the state of a system over time, especially when the system is complex or uncertain. It works by representing possible states with many "particles," which are samples of potential states. As new observations come in, the filter updates these particles to reflect the observations better, often eliminating less likely states and focusing on more probable ones. This technique is widely used in fields like robotics and navigation, where accurate tracking of a moving object or system is essential despite noise and uncertainty in measurements.