Distributed localization using Levenberg-Marquardt algorithm

Abstract

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 as compared to first-order methods. The performance of the algorithm is demonstrated with extensive simulations in Julia in which it is shown that our method outperforms distributed methods that are based on approximate maximum likelihood formulations.

Keywords

• Distributed localization
• Maximum likelihood estimation
• Message passing
• Dynamic programming
• Levenberg-Marquardt
• Nonlinear least-squares