MATLAB Implementation of Particle Filter Algorithm

In this article, we explore the implementation of particle filtering, which is a filter used for estimating states in nonlinear, non-Gaussian systems. The core concept involves using a set of random samples (called particles) to approximate the target distribution through importance resampling techniques. The particle filter algorithm finds applications in various domains such as target tracking, robot localization and navigation, and signal processing. In this study, we implemented the fundamental particle filter algorithm in MATLAB, specifically the SIR (Sequential Importance Resampling) approach. The algorithm utilizes importance sampling where particles are weighted according to their importance weights to approximate the target distribution. Our implementation includes key components: particle initialization, importance weight calculation based on measurement likelihood, systematic resampling to prevent particle degeneracy, and state estimation through weighted averaging. We developed and tested this program to verify its correctness and effectiveness, demonstrating successful target state estimation with high accuracy. In summary, this research successfully implements the basic particle filter algorithm and provides a foundation for further studies in related fields. We believe this algorithm will see broader applications in future research, with potential extensions including adaptive resampling strategies and improved proposal distributions for enhanced performance.