Distributed and decentralized Kalman filtering for Cascaded Fractional order systems

Abstract

This paper presents a distributed Kalman filter algorithm for cascaded systems of fractional order. A functional distribution of a large scale system and of the state estimation algorithm leads to smaller and scalable nodes with reduced memory and computational effort. Since each subsystem performs its calculations locally, a central processing node is not needed. All data which are required by subsequent nodes are communicated to them unidirectionally. Also, a comparison between the Fractional Kalman Filter (FKF) and the Cascaded Fractional Kalman Filter (CFKF) is given by an example.