Distributed localization of heterogeneous agents with uncertain relative measurements and communications

Abstract

This paper presents a distributed filtering algorithm for networked agents modeled with independent linear state models and measurements. In addition to local state measurements, agents utilize relative state measurements and communicated state estimates to improve their individual state estimates. These kind of measurement models are motivated by the problem of localization in sensor networks and multi-vehicle robotics. In this paper, we explicitly consider that all the state models, measurements and communications are subject to disturbances and we propose a minimum-energy filtering approach to derive the state estimates. The filters are distributed due to the practical requirement that th..