Extended Kalman Filter Algorithm

The Extended Kalman Filter (EKF) algorithm finds widespread application across numerous technical domains including robotics, wireless communications, autonomous vehicles, and aerospace systems. Its modular design allows for easy adaptation to different scenarios by simply modifying the background context and system models. For developers with foundational knowledge in state estimation, this implementation provides a streamlined version with removed background dependencies, making it easier to understand the core algorithm structure and apply it to specific use cases. Key implementation aspects include: - Linearization of nonlinear systems through Jacobian matrix calculations - Prediction and update cycles handling state estimation with Gaussian noise - Modular design allowing customization of state transition and observation models The code structure emphasizes clarity with separated functions for prediction steps (handling state propagation) and update steps (incorporating new measurements). This clean implementation serves as an excellent starting point for research and practical applications in dynamic system estimation.