Particle Filter Based on Suboptimal Bayesian Estimation for Nonlinear Non-Gaussian Conditions

In this paper, we investigate particle filters based on suboptimal Bayesian estimation for nonlinear non-Gaussian conditions, implementing comprehensive MATLAB simulations. We examine the working principles and advantages of particle filters, particularly their application in nonlinear and non-Gaussian scenarios. The implementation involves sequential Monte Carlo methods where particles represent probability distributions, with systematic resampling techniques to mitigate degeneracy issues. Additionally, we introduce the suboptimal Bayesian estimation algorithm, discussing its practical integration into particle filtering through importance sampling and weight updating mechanisms. Key MATLAB functions include particle resampling using systematic methods, state transition modeling with nonlinear equations, and likelihood calculation for non-Gaussian noise distributions. Ultimately, we validate the effectiveness of our proposed approach through MATLAB simulations demonstrating state estimation accuracy and computational efficiency metrics, including comparative analysis with traditional filtering methods under various noise conditions.