MATLAB Implementation of Kalman Filter Algorithm

This documentation presents a complete implementation of the Kalman filter algorithm in MATLAB. The implementation supports multi-dimensional data processing for both input and output operations, making it suitable for various state estimation applications involving vector quantities. The code includes detailed inline comments that explain key algorithmic components such as: - State prediction equations using system dynamics models - Measurement update steps incorporating sensor data - Covariance matrix propagation and innovation calculations - Gain computation for optimal filtering These annotations significantly enhance code comprehension by clarifying the mathematical foundations and implementation logic. They serve as valuable documentation for understanding the algorithm's workflow, maintaining the codebase, and facilitating future improvements. The comments specifically detail: - Matrix operations for state transition and observation models - Handling of process and measurement noise covariance matrices - Recursive filtering procedures for real-time data processing Proper code annotation is crucial for ensuring long-term maintainability, enabling other developers to understand the implementation approach, and supporting algorithm modifications or extensions while preserving code readability and technical accuracy.