Federated Kalman filter simulation results

This paper describes federated filter applications to integrated, fault-tolerant navigation systems, with emphasis on real-time implementation issues and numerical simulation results. The federated filter is a near-optimal estimator for decentralized, multisensor data fusion. Its partitioned estimation architecture is based on theoretically sound information-sharing principles. It consists of one or more sensor-dedicated local filters, generally operating in parallel, plus a master combining filter. The master filter periodically combines (fuses) the local filter solutions to form the best total solution. Fusion generally occurs at a reduced rate, relative to the local measurement rates. The method can provide significant improvements in fault tolerance, data throughput, and system modularity. Numerical simulation results are presented for an example multisensor navigation system. These results demonstrate federated filter performance characteristics in terms of estimation accuracy, fault tolerance, and computation speed.