A Distributed and Maximum-Likelihood Sensor Network Localization Algorithm Based Upon a Nonconvex Problem Formulation

Abstract

We propose a distributed algorithm for sensor network localization, which is based upon a decomposition of the nonlinear nonconvex maximum likelihood (ML) localization problem. Decomposition and coordination are obtained by applying the alternating direction method of multipliers (ADMM), to provide a distributed, synchronous, and nonsequential algorithm. When penalty coefficients are locally increased under specific conditions, the algorithm is proved to converge irrespective of the chosen starting point. It is also shown to be fast and accurate, providing a performance, which is equivalent to that of a centralized solver. In the comparison with existing literature on distributed sensor network localization, the proposed method involves a much lighter local processing effort, an improved robustness in non-Gaussian scenarios, and a better adherence to the original problem.