Recursive State Estimation for Complex Networks With Energy Harvesting Constraints and Decode-and-Forward Relays

Abstract

This article examines the recursive estimation issue for a class of complex networks that incorporates decode-and-forward (DaF) relays and energy harvesting (EH) techniques. The random intercoupling topologies are captured by Gaussian noise. Owing to the insufficient transmission capacity of sensors, DaF relays are implemented to connect sensors with remote estimators, augmenting the transmission range and improving communication quality. The energy required for signal transmission can be supplied through EH techniques deployed at sensors and relays. This study focuses on designing a recursive state estimator aimed at guaranteeing accurate estimation performance. An upper bound for the estimation error covariance matrix is formulated via two recursive equations, and subsequently minimized by properly designing the estimation gain. Moreover, the developed estimator is evaluated through detailed theoretical analysis, with emphasis on its uniform boundedness and monotonic behavior. Simulated examples confirm the efficacy of the underlying distributed estimator.