Collection of Particle Filter Source Code

This documentation presents a curated collection of particle filter source code implementations. The repository contains multiple particle filter variants such as PF (Standard Particle Filter), RPF (Regularized Particle Filter), EKFPF (Extended Kalman Filter Particle Filter), APF (Auxiliary Particle Filter), and others. These implementations demonstrate key algorithmic differences including resampling techniques, proposal distribution designs, and state estimation methods. By studying these source codes, researchers can gain deep insights into the comparative advantages and limitations of different particle filter approaches, along with their performance characteristics across various application scenarios. The codebase also serves as a practical foundation for algorithm modification and optimization, enabling improvements in computational efficiency and estimation accuracy through techniques like adaptive resampling, likelihood optimization, and parallel computing implementations. This collection provides significant reference value for both learning particle filter fundamentals and advancing research in sequential Monte Carlo methods.