Implementation of Extended Kalman Filter (EKF) in MATLAB

In this article, we will conduct an in-depth exploration of the Extended Kalman Filter algorithm's applications in target tracking and distance estimation. First, we will introduce the fundamental principles and mathematical models underlying the Extended Kalman Filter algorithm, including its linear approximation approach for nonlinear systems using Jacobian matrices. Then, we will provide detailed explanations on implementing this algorithm for target tracking and distance measurement scenarios, with specific MATLAB code examples demonstrating state prediction (using the 'predict' function) and measurement update steps (utilizing the 'correct' method). We will analyze the algorithm's advantages in handling nonlinear systems and discuss its limitations regarding computational complexity and linearization errors. Additionally, we will examine practical challenges such as sensor noise modeling and state initialization, along with corresponding solutions involving covariance tuning and adaptive filtering techniques. Finally, we will summarize the applications of Extended Kalman Filter in target tracking and distance estimation, and prospect its future development trends including integration with machine learning approaches and real-time implementation optimizations.