Kalman Filter Algorithm Implementation

This article presents a Kalman filter algorithm-based software solution, implemented as a computer program operating within the MATLAB environment. The Kalman filter algorithm represents an optimal estimation technique rooted in Bayesian theorem and Gaussian distribution theory, enabling precise estimation and prediction of dynamic system variations. This algorithm finds extensive applications in control systems, signal processing, robotics, and related technical domains. The software's design and development focus on providing users with an efficient and accurate data processing tool, featuring key implementations such as state prediction equations (x_k = F*x_{k-1} + B*u_k), measurement update routines, and covariance matrix calculations. The program includes essential functions for handling process noise (Q matrix) and measurement noise (R matrix), allowing users to better understand and apply Kalman filtering principles through practical MATLAB code examples and real-time data filtering demonstrations.