Sensors Selection via a Distributed Reputation Mechanism: An Information Fusion Approach

Abstract

In this paper, an adaptive sensor selection architecture is developed to deal with distributed state estimation problems for multi-agent networked systems consisting of three different classes of nodes (plants, sensors and agents). Specifically, the problem of adequately fusing the sensors data coming from the plants and delivered to the agents, is addressed by evaluating their trustworthiness. This is achieved by exploiting a well-established approach in the power electronics: the Perturb&Observe algorithm that in the present framework allows one to select the more adequate group of sensors so as to compute at each time instant the best state estimate according to a given performance index. Some simulations are finally reported to testify the effectiveness of the proposed methodology.