Distributed fusion filtering for multi-sensor systems under time-correlated fading channels and energy harvesters

In this paper, the distributed fusion filtering issue is investigated for multi-sensor systems with the constraints from both time-correlated fading channels and energy harvesters. A specific scenario is considered where the sensors can harvest energy from the natural environment and may consume a certain amount of energy when transmitting measurements to the filters. In order to properly deal with the energy supply relationship between a battery and multiple sensors, a dynamic energy-allocated rule is proposed in this paper, i.e., the storage battery provides energy to sensors in order of different sensors' priorities. Additionally, the channel fading phenomenon is also taken into consideration and the fading coefficient is described by a dynamic process. In this paper, we are committed to designing a local filter such that, under the effects of the time-correlated fading channels and energy harvesters, an upper bound on the local filtering error covariance is firstly derived by using the mathematical induction and then the upper bound is minimized by designing the local filter gain. Next, the covariance intersection approach is employed to obtain the fusion estimates. Finally, a simulation is provided to verify that the presented filtering strategy is feasible and effective.(c) 2023 The Franklin Institute. Published by Elsevier Inc. All rights reserved.

Automated summary

In this paper, the distributed fusion filtering issue is investigated for multi-sensor systems with the constraints from both time-correlated fading channels and energy harvesters. A dynamic energy-allocated rule is proposed to properly deal with the energy supply relationship between a battery and multiple sensors. The local filter is designed to minimize the upper bound of the local filtering error covariance under the effects of the time-correlated fading channels and energy harvesters, and the fusion estimates are obtained using the covariance intersection approach.