An overview of decentralized Kalman filter techniques

The federated Kalman filter, which combines data from multiple Kalman filters, is discussed. The federated filter can provide performance equal to that of a single Kalman filter that integrates all the independent sensor data in the system. The advantage is that a single filter is impractical with existing sensors. The federated filter is practical, but for true optimal performance it is necessary that all Kalman filters contain the same process model and make their covariance matrices available on the serial data bus. The federated filter can be reconfigured to provide a less optimal solution with a higher degree of fault tolerance. The application of the federated filter to combine data from two Kalman filters in a navigation system is simulated, and results are provided.<<ETX>>