A Comprehensive Toolbox for Filtering Algorithms Implementation

This article presents a comprehensive filtering algorithms toolbox designed for implementation and modification of various estimation techniques. The toolbox includes implementations of fundamental filtering algorithms: Kalman Filter (KF), Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Unscented Particle Filter (UPF). For demonstration purposes, we utilize GSM signal processing scenarios to simulate and compare the performance of these filtering algorithms. The implementations feature object-oriented design with configurable noise parameters, state transition models, and measurement functions. Each algorithm is implemented with proper initialization procedures, prediction steps (using state transition equations), and update steps (incorporating measurement data). The simulation results provide valuable insights into algorithm performance under different noise conditions and system dynamics, serving as an excellent reference for understanding filter behavior and performance characteristics in practical applications.