Particle Filter Tracking Algorithm Demonstration Program

This program serves as a demonstration of the particle filter tracking algorithm. Particle filter algorithm is a tracking estimation method specifically designed for nonlinear and non-Gaussian scenarios. This expert-level implementation focuses on demonstrating the core principles of particle filter tracking, including sequential importance sampling and systematic resampling methods. The implementation features key components such as: - State transition models with process noise handling - Measurement likelihood functions for observation updates - Effective sample size monitoring for resampling triggers - Systematic resampling to prevent particle degeneracy Additionally, we provide valuable supplementary information including the algorithm's advantages (handling nonlinear systems, multi-modal distributions) and limitations (computational complexity, sample impoverishment). Practical guidance is included for real-world applications, covering parameter tuning strategies and convergence monitoring techniques. We aim to share our expertise and knowledge through this program to help users better understand and implement particle filter algorithms in their projects.