Learning Classical Particle Filter Algorithms

This article introduces the particle filter algorithm, a classical learning method for state estimation. Through this guide, you will gain a thorough understanding of particle filter concepts and theoretical foundations. Particle filtering is a Monte Carlo-based state estimation technique with extensive applications across various domains, including robotics, computer vision, signal processing, and many others. Therefore, for individuals seeking to deepen their knowledge in this field, particle filters constitute an essential component. The article provides detailed explanations of fundamental particle filter concepts, underlying principles, and algorithmic workflows. Key implementation aspects covered include: - The systematic sampling-importance-resampling (SIR) framework - Weight calculation and normalization procedures - Particle propagation using state transition models - Effective resampling techniques to mitigate particle degeneracy We explore core algorithmic components such as: - Importance sampling strategies for proposal distribution selection - Likelihood evaluation methods for weight updates - Resampling algorithms (multinomial, stratified, systematic) - State estimation through weighted particle averaging This comprehensive approach helps readers master particle filter technology through both theoretical understanding and practical implementation considerations, including code structure patterns and optimization techniques for real-world applications.