Comparative Simulation Code for PF, EPF, and UPF Algorithms

This documentation presents comparative simulation codes for PF, EPF, and UPF algorithms. These simulations provide clear visualizations of algorithmic differences, performance characteristics, and relative strengths/weaknesses. While the code is ready-to-run without modifications, we recommend deeper analysis to fully understand implementation methodologies and performance metrics. The codebase implements core filtering components including: - Systematic resampling procedures for particle degeneracy prevention - Nonlinear state transition models with process noise integration - Measurement update mechanisms using likelihood functions - UT (Unscented Transform) implementations for UPF sigma point generation Through comparative execution, users can evaluate: - Estimation accuracy under nonlinear/non-Gaussian conditions - Computational complexity and real-time feasibility - Robustness to initialization errors and model mismatches - Memory usage and particle efficiency trade-offs Such analysis not only deepens understanding of Bayesian filtering principles but also provides practical insights for algorithm selection and optimization in real-world applications like target tracking, financial modeling, and robotic localization. The modular code structure allows easy extension to custom system models and performance metrics.