Basic Particle Filter Implementation in MATLAB

In this documentation, we provide a detailed walkthrough of a basic particle filter implementation using MATLAB, which leverages importance sampling principles. The particle filter is a Bayesian estimation technique that approximates posterior distributions using a set of weighted particles. This implementation serves as an excellent educational example for beginners, designed to enhance understanding of particle filter mechanics through practical code demonstration. The program structure includes key components: particle initialization using random sampling, importance weight calculation based on measurement likelihood, systematic resampling to prevent degeneracy, and state estimation through weighted averaging. We guide you through each algorithmic step with corresponding code explanations, covering essential functions like particle propagation through state transition models, weight normalization, and effective sample size monitoring. The implementation demonstrates how to handle sequential Bayesian filtering with code segments showing real-time weight updates and resampling triggers. By examining this example, you will gain practical insights into modifying the filter for custom applications, including adjusting particle counts, changing dynamic models, or incorporating different resampling strategies. After running this program, you'll be equipped to adapt the core algorithm for specific state estimation requirements while maintaining computational efficiency.