Particle Filter for Target Tracking with 100 Monte Carlo Simulations

In this article, we present a detailed implementation of particle filter algorithms for target tracking through Monte Carlo simulations, conducting 100 independent simulation runs. Our implementation generates comprehensive trajectory visualization plots and error analysis curves to evaluate the filter's performance characteristics. The simulation framework employs systematic resampling techniques and importance sampling methods to maintain particle diversity while estimating posterior distributions. We analyze key performance metrics including root mean square error (RMSE) and tracking consistency, discussing optimization strategies such as adaptive particle number adjustment and effective likelihood function design. Comparative analysis with conventional tracking methods (Kalman filters and multiple model approaches) demonstrates the particle filter's superiority in handling nonlinear systems and non-Gaussian noise environments. The article provides practical insights into implementing particle filters with code examples covering state initialization, weight computation, and resampling procedures, enabling readers to apply these techniques in real-world target tracking applications.