We propose an anchorless distributed technique for estimating the centroid of a network of agents from noisyrelativemeasurements. The positions of the agents are then obtained relative to the estimated centroid. The usual approach to multi-agent localization assumes instead that one anchor agent exists in the network, and the other agents’ positions are estimated with respect to the anchor. We show that our centroid-based algorithm converges to the optimal solution, and such a centroid-based representation produces results that are more accurate than anchor-based ones, irrespective of the selected anchor.
|Titolo:||Distributed Centroid Estimation from Noisy Relative Measurements|
|Data di pubblicazione:||2012|
|Digital Object Identifier (DOI):||10.1016/j.sysconle.2012.04.008|
|Appare nelle tipologie:||1.1 Articolo in rivista|