MATLAB Example of Kalman Filter Prediction Algorithm

In the following example, we demonstrate how to implement a Kalman filter prediction algorithm using MATLAB. The Kalman filter is a widely-used state estimation method originally designed for aerospace applications, but now extensively applied across various domains. This example showcases how to use the Kalman filter algorithm to predict future states of a physical quantity, helping users better understand both the application scenarios and fundamental principles of the algorithm. The implementation typically involves defining state transition matrices (A), measurement matrices (H), process noise covariance (Q), and measurement noise covariance (R). Key MATLAB functions like 'kalmand' or custom implementations using matrix operations are commonly employed. The algorithm operates through two main steps: prediction (projecting state estimates forward using system dynamics) and update (correcting estimates with new measurements). This example includes code segments demonstrating parameter initialization, recursive filtering cycles, and result visualization techniques.