Extended Kalman Filter Algorithm Implementation

This program provides a robust foundation for understanding Extended Kalman Filter (EKF) algorithms through practical implementation. It enables deeper insights into the algorithm's underlying principles and operational mechanisms, including linearization techniques for nonlinear systems. Users can modify algorithm parameters such as process noise covariance (Q) and measurement noise covariance (R) to observe their impact on estimation accuracy. The codebase contains extensive inline comments explaining key functions including state prediction updates, Jacobian matrix calculations, and measurement correction steps. Structured as a modular framework, it supports easy customization for specific applications while demonstrating proper handling of nonlinear system dynamics through first-order Taylor series approximations. This implementation serves as both an educational tool and a foundational template for developing customized EKF-based solutions.