Information Fusion Based on Kalman Filter with MATLAB Implementation Examples

This article explores information fusion based on Kalman filters, an algorithmic approach for data processing that combines measurements from multiple sensors to enhance result accuracy. We will demonstrate practical implementation using MATLAB applications, including core functions like kalman for state estimation and sensor fusion techniques.

In practical applications, information fusion finds extensive use across various domains such as robotics, autonomous vehicles, and sensor networks. By fusing data from multiple sensors through algorithms like the Kalman filter (which employs prediction-correction cycles and covariance matrix updates), systems achieve higher accuracy and reliability, ultimately improving overall performance.

This article covers the fundamental principles and applications of Kalman filters, along with detailed MATLAB implementation methodologies. We will provide practical code examples demonstrating key aspects such as state-space modeling, measurement updates using MATLAB's filter functions, and real-world application scenarios to help readers better understand information fusion concepts and their practical implementations.