Robust fusion filtering over multisensor systems with energy harvesting constraints

In this paper, a general theoretical framework is established for the robust fusion filtering problem of discrete time-varying stochastic multisensor systems under energy harvesting constraints. The energy harvesting technology is utilized to provide the needed energy for persistently maintaining the operation of the multisensor systems. The energy level at the energy harvester is characterized by a random variable obeying a certain probability distribution. For the communication between sensors and filters, we consider a scenario where the measurements received by sensors are broadcasted via networks and then obtained by filters according to a set of preassigned communication links. The aim of this paper is to design the fusion filter over a multisensor system with locally minimized variance of the estimation error. Specifically, the local filter is firstly designed such that, in the presence of energy harvesting constraints and parameter uncertainties, an upper bound on the filtering error covariance is guaranteed and subsequently minimized by appropriately choosing the filter parameters at each time instant. Then, all the local estimates obtained by local filters are fused by using the covariance intersection fusion strategy for fusion estimation purposes. Finally, an illustrative simulation is carried out to demonstrate the usefulness of the proposed fusion filtering scheme.