Comparative Analysis of Particle Filter and Extended Kalman Filter

In this article, we conduct a comparative analysis of particle filter (PF) and extended Kalman filter (EKF) algorithms. To clearly illustrate the implementation process, we develop corresponding MATLAB code with practical case demonstrations. The implementation approach covers key aspects such as state initialization, prediction steps using system models, and update steps incorporating measurement data. We discuss the advantages and disadvantages of each filter, including PF's capability to handle non-linear/non-Gaussian systems through sequential Monte Carlo methods versus EKF's efficiency for mildly non-linear Gaussian systems through local linearization. Additionally, we provide practical implementation tips and recommendations for parameter tuning, resampling strategies for particle filters, and Jacobian matrix calculations for extended Kalman filters. If you have any questions or suggestions regarding these implementations, we welcome discussions to further explore these interesting and practical filtering techniques.