Algorithm Implementation for Predicting Circular Motion Using Kalman Filter

In this article, we demonstrate how to implement the Kalman filter algorithm to predict circular motion trajectories. The algorithm analyzes key parameters of circular motion—such as angular velocity and turning radius—to forecast future positions and directions. For implementation, circular motion must be modeled as a state-space system where the Kalman filter recursively estimates state variables. Key implementation steps include defining state vectors (e.g., position, velocity, acceleration) and measurement models, while incorporating process noise covariance for robustness. Additionally, preprocessing steps like data normalization and outlier removal, along with postprocessing techniques such as smoothing and residual analysis, are essential for optimizing algorithm performance. Through this guide, you will learn to implement the core prediction logic using Python or MATLAB, with emphasis on tuning filter parameters and validating results through trajectory visualization and error metrics.