MATLAB Implementation of Particle Filter with Multiple Sampling Strategies

This program provides three distinct sampling strategy options for particle filter implementation, enabling customization based on your specific data processing needs. The sampling strategies - systematic resampling, multinomial resampling, and residual resampling - are implemented through MATLAB's vectorized operations and probability distribution functions. Each method employs different approaches to particle selection and redistribution: systematic sampling uses deterministic intervals for uniform coverage, multinomial sampling applies weighted random selection via cumulative distribution functions, while residual sampling combines deterministic and stochastic components for improved efficiency. The implementation utilizes MATLAB's rand() and randperm() functions for randomization, with custom weighting algorithms that normalize particle weights using sum() and cumsum() operations. This codebase incorporates latest computational techniques ensuring high efficiency through matrix operations and accuracy through proper weight normalization and resampling procedures. For large-scale data processing applications, this optimized implementation serves as an ideal choice, featuring configurable parameters for particle count, noise models, and state transition functions that can be adapted to various tracking and estimation scenarios.