Adaptive consensus-based algorithms for fast estimation from relative measurements*

In this work we propose a novel asynchronous algorithm to solve the problem of optimal estimating the position of each agent in a network from relative noisy vectorial distances with its neighbours. This algorithm is based on the combination of two asynchronous algorithms which have been recently proposed in the literature for localization problems. Specifically, we analyze various switching strategies from an asynchronous algorithm which exhibits a fast transient but does not convergence to an optimal solution, to another asynchronous algorithm which is slower in the transient but reaches an optimal solution exponentially. We provide a large set of simulations to compare the different switching strategies and to verify which one performs better.