Performance Comparison of Extended Kalman Filter and Particle Filter for Target Tracking

In this article, the author discusses the performance comparison between Extended Kalman Filter (EKF) and Particle Filter for target tracking applications, which presents a fascinating technical topic. While the author doesn't provide detailed explanations of these filtering algorithms, readers can refer to academic literature or specialized courses to understand their mathematical foundations and implementation approaches. From a coding perspective, EKF typically involves linearizing nonlinear systems using Jacobian matrices and maintaining Gaussian assumptions, while Particle Filter uses sequential Monte Carlo methods with importance sampling and resampling techniques. For readers deeply interested in target tracking, additional research materials can help better understand and implement these algorithms - potentially involving state prediction, measurement updates, and covariance management for EKF, or particle propagation, weight calculation, and resampling steps for Particle Filter. Although the author decided to share this content publicly rather than keeping it private, we appreciate their contribution in highlighting this important comparison, providing an opportunity to explore advanced filtering techniques in tracking systems.