Kalman Filter (KF), Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Particle Filter (PF), and Unscented Particle Filter (UPF) Program Codes

This document presents a concise introduction to program codes implementing various filtering algorithms, including Kalman Filter (KF), Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Particle Filter (PF), and Unscented Particle Filter (UPF). While all these algorithms serve for state variable estimation, their respective application scopes and implementation approaches differ significantly. The KF implementation typically features linear state transition and measurement models with covariance propagation, while EKF introduces first-order Taylor approximations for nonlinear systems through Jacobian matrix calculations. UKF employs unscented transformation using sigma points to handle nonlinearities without derivative computations, and PF utilizes sequential Monte Carlo methods with importance sampling and resampling procedures. UPF combines UKF's proposal distribution with PF's sampling framework for improved efficiency. Through this document, readers can access specific implementation details including algorithm initialization, prediction-update cycles, and parameter tuning strategies, gaining deeper insights into their theoretical principles and comparative advantages. Additionally, readers will learn how to apply these filtering algorithms to various domain-specific problems such as robot localization and navigation, autonomous driving systems, and multi-sensor data fusion applications. The code implementations include modular functions for state prediction, measurement updates, and noise handling, with practical examples demonstrating real-world integration scenarios. Overall, this document serves as a comprehensive code repository that enables readers to master both the implementation and application of diverse filtering algorithms across different engineering domains.