# Distributed, simple and stable network localization

arXiv: https://arxiv.org/abs/1406.5223 · HTML: https://mlsanity.com/paper/1406.5223/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2014.

## Built on by (most cited first, 2 of 2)

- [STRONG: Synchronous and asynchronous RObust Network localization, under Non-Gaussian noise](https://mlsanity.com/paper/2110.00594/) (2021)
- [ISEE.U: Distributed online active target localization with unpredictable targets](https://mlsanity.com/paper/2210.09107/) (2022)

## Abstract (arXiv)

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.