Extended Kalman Filter MATLAB Implementation

In this learning program, we implement the Extended Kalman Filter in MATLAB to facilitate better understanding of its algorithmic principles. Beyond its simplicity and user-friendly design, we introduce configurable input parameters that allow precise control over the algorithm's behavior. The implementation includes detailed explanations of each algorithmic step: initialization, prediction (using state transition functions), measurement update (with observation models), and covariance matrix updates. We demonstrate key MATLAB functions such as 'ekf' for core filtering operations, 'jacobian' for linearization of nonlinear systems, and parameter tuning methods for optimal performance. Through comprehensive case studies covering various scenarios like sensor fusion and nonlinear system tracking, we illustrate different aspects of the algorithm. This hands-on approach provides valuable practical experience, enabling deeper comprehension of Kalman Filter principles and applications. We are confident that this program will help you master Kalman Filter fundamentals, establishing a solid foundation for future research and engineering projects.