Applications of Centralized and Federated Kalman Filtering in Integrated Navigation Systems

In integrated navigation systems, centralized and federated Kalman filtering represent two primary approaches for data fusion and filtering. The centralized Kalman filter processes all sensor measurements collectively in a single fusion algorithm, typically implementing a global state estimation through matrix operations that handle covariance propagation and Kalman gain calculation. In contrast, federated Kalman filtering employs distributed processing where individual sensor data undergoes local Kalman filtering before fusion, utilizing a master filter to combine local estimates through covariance intersection or information fusion algorithms. A typical implementation would involve separate filter modules for different sensors (IMU, GPS, etc.) with fusion weights determined by their respective covariance matrices. These methodologies enhance navigation system accuracy and reliability through optimal estimation techniques, finding extensive applications in aviation, marine navigation, automotive systems, and other domains where multi-sensor data integration is critical.