Distributed, simple and stable network localization

TL;DR

This paper introduces a simple, stable, and distributed algorithm for sensor network localization that optimizes a nonconvex likelihood function without parameter tuning, outperforming existing methods in accuracy and communication efficiency.

Contribution

It presents a novel distributed algorithm based on Majorization-Minimization that directly optimizes the likelihood function with guaranteed stability and no need for parameter tuning.

Findings

Outperforms state-of-the-art algorithms in accuracy

Reduces communication cost in network localization

Ensures stability through MM approach

Abstract

We propose a simple, stable and distributed algorithm which directly optimizes the nonconvex maximum likelihood criterion for sensor network localization, with no need to tune any free parameter. We reformulate the problem to obtain a gradient Lipschitz cost; by shifting to this cost function we enable a Majorization-Minimization (MM) approach based on quadratic upper bounds that decouple across nodes; the resulting algorithm happens to be distributed, with all nodes working in parallel. Our method inherits the MM stability: each communication cuts down the cost function. Numerical simulations indicate that the proposed approach tops the performance of the state of the art algorithm, both in accuracy and communication cost.