Implementation of Particle Filter Algorithm

In this article, we present a comprehensive MATLAB implementation of particle filtering algorithms, accompanied by Chinese annotations to facilitate better understanding. We begin by explaining the core principles and practical applications of particle filters, highlighting their significance in state estimation problems across various domains. The implementation covers key algorithmic components including particle initialization using random sampling techniques, prediction steps employing state transition models, update phases incorporating likelihood calculations based on observation data, and systematic resampling procedures to mitigate particle degeneracy. Each code segment demonstrates practical MATLAB programming approaches such as vectorized operations for efficient computation and probability density function handling. Readers will gain insights into practical implementation considerations including computational efficiency optimization and parameter tuning strategies, enabling them to adapt this framework to real-world projects involving nonlinear/non-Gaussian estimation challenges.