Improved fractional-order distributed Kalman Filter for use in time-delay sensor networks

Abstract

Presently, distributed network systems are extensively used in a wide range of applications such as war field supervision, target tracing and positioning, error recognition, etc. However, a mechanism such as Kalman is needed to resolve issues such as configuration of topologies at the physical layer of sensor networks and delay in measurement time and data transmission in order to guarantee correctness and accuracy of parameter measured by the sensors. On the other hand, fractional calculus which is a generalization of integer order operators allows for highly precise modeling of physical systems. Thus, a new fractional-order distributed Kalman filter algorithm is presented for state estimation in measurement time-delay sensor networks in this paper. Therefore, at first fractional distributed Kalman filter algorithms and then their performance metrics such as means squared deviation and average will be evaluated to investigate feasibility of the algorithm. Finally, simulations show that performance of the proposed algorithm in terms of accuracy and efficiency has considerably improved as compared with previously proposed approaches such as conventional fractional-order Kalman filter.