Extended Kalman Filter - Basic Implementation with Detailed Code Comments

This implementation contains essential code for the Extended Kalman Filter algorithm, featuring detailed inline comments to facilitate understanding. The code demonstrates key components including state transition functions, measurement models, and covariance matrix updates. By examining this implementation alongside core Kalman filter principles, you'll develop a solid grasp of the prediction-correction cycle and how to apply it to real-world problems. The code incorporates practical optimization techniques such as numerical stability handling and efficient matrix operations. Notable functions include the state prediction step using nonlinear system models and the measurement update with Jacobian matrix calculations for linearization. This implementation serves as an educational foundation that can be extended and optimized for specific applications. We welcome improvements and enhancements to this codebase to benefit the broader technical community.