Distributed Localization Using Levenberg-Marquardt Algorithm

Abstract In this paper, we propose a distributed algorithm for sensor network localization based on a maximum-likelihood formulation. It relies on the Levenberg–Marquardt algorithm where the computations are distributed among different computational agents using message passing, or equivalently dynamic programming. The resulting algorithm provides a good localization accuracy, and it converges to the same solution as its centralized counterpart. Moreover, it requires fewer iterations and communications between computational agents compared to first-order methods. The performance of the algorithm is demonstrated with extensive simulations in Julia, in which it is shown that we outperform distributed methods that are based on approximate maximum likelihood formulations.