Particle Filter

Particle filters are a family of algorithms used for state estimation in dynamic systems, aiming to track the probability distribution of a hidden state based on noisy observations. Current research emphasizes improving particle filter efficiency and robustness, particularly in high-dimensional spaces, through techniques like Rao-Blackwellization, optimized transport maps, and neural network augmentations such as those incorporating normalizing flows or Langevin proposals. These advancements are driving applications across diverse fields, including robotics (localization, object tracking, control), data assimilation, and even solving complex problems using large language models.