Particle Filter Implementation through Monte Carlo Simulation for Recursive Bayesian Filtering
Particle filters utilize Monte Carlo simulation to achieve recursive Bayesian filtering, eliminating the need for linearity or Gaussian noise assumptions. This makes them suitable for any nonlinear system representable by state-space models, offering broader applicability than Kalman filters. The provided MATLAB examples demonstrate practical implementations including target tracking, parameter identification, and robotic SLAM applications with detailed code structure explanations.