PFbased
PFbased is an adjective used to describe methods and systems that rely on particle-filter-based estimation, i.e., particle filters or Sequential Monte Carlo methods, to approximate probability distributions over latent variables in dynamic processes. The term appears in fields such as robotics, computer vision, signal processing, and time-series analysis. In practice, PFbased approaches are often written as “PF-based” or encountered as PFbased in more compact notation.
Particle filters were developed to address nonlinear and non-Gaussian state estimation problems. They represent the posterior
Core methodology involves initializing particles, predicting their states through a transition model, weighting each particle by
Applications of PFbased methods span localization and mapping in robotics (for example, Monte Carlo localization), object
In relation to other techniques, PFbased approaches contrast with Kalman-filter-based methods and with largely analytic Bayesian