MATLAB Implementation of Kalman Filter Algorithm

In this document, I am sharing an original MATLAB implementation of the Kalman filter algorithm. This code has been personally tested and demonstrated excellent performance in practical applications. The implementation features a standard Kalman filter structure with key functions including state prediction, measurement update, and covariance matrix propagation. The core algorithm follows the recursive Bayesian estimation approach, handling both prediction and correction steps efficiently.

Kalman filtering is a powerful technique for data processing and control systems, widely applied across numerous engineering fields. This optimal filtering algorithm excels at estimating system states and predicting future states while effectively reducing noise and measurement errors. The MATLAB code implements the fundamental Kalman filter equations: x_k = F*x_{k-1} + B*u_k (prediction) and K_k = P_k*H'*(H*P_k*H' + R)^{-1} (Kalman gain calculation).

The implementation includes proper initialization of state vectors and covariance matrices, with configurable parameters for different system models. For those interested in deeper understanding, I recommend consulting relevant academic papers and textbooks on optimal estimation theory. I believe this well-structured MATLAB implementation will be valuable for your research and engineering projects, providing a solid foundation for further customization and extension.

Thank you for your interest in this technical resource!