Comparative Analysis and Implementation of Particle Filter Resampling Algorithms in MATLAB

This article provides a comprehensive comparative analysis of particle filter resampling algorithms and demonstrates their MATLAB implementation. We examine the advantages and disadvantages of different resampling algorithms - such as multinomial, systematic, residual, and stratified resampling methods - and guide the selection of appropriate algorithms for specific problem domains. The implementation section covers key MATLAB functions including weight normalization using cumsum(), random number generation with rand(), and index selection mechanisms for particle redistribution. We discuss parameter optimization strategies for improving algorithmic performance, such as adjusting the effective sample size threshold and resampling frequency. Practical case studies illustrate real-world applications across various domains, showcasing how to implement these algorithms using MATLAB's vectorization capabilities for efficient computation. Finally, we explore future research directions and potential development trends in particle filter resampling methodologies, including adaptive resampling techniques and hybrid approaches that combine multiple strategies.