Federated Kalman Filter Simulation Results

Abstract

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, multi-sensor data fusion. Its decentralized estimation archi- tecture is based on theoretically sound information-sharing principles. A federated filter consists of one or more sensor- dedicated local filters, generally operating in parallel, plus a master combining filter. The master filter periodically com- bines (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 sig- nificant improvements in data throughput, fault tolerance, and system modularity. Numerical simulation results are pre- sented for an example multi-sensor navigation system. These results demonstrate federated filter performance characteristics in terms of estimation accuracy, fault-tolerance, and computation speed. This work was supported by the Defense Small Business Innovation