# Simple and fast convex relaxation method for cooperative localization in sensor networks using range measurements

arXiv: https://arxiv.org/abs/1408.4728 · HTML: https://mlsanity.com/paper/1408.4728/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 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)
- [Generalizing Trilateration: Approximate Maximum Likelihood Estimator for Initial Orbit Determination in Low-Earth Orbit](https://mlsanity.com/paper/2407.15180/) (2024)

## Abstract (arXiv)

We address the sensor network localization problem given noisy range measurements between pairs of nodes. We approach the non-convex maximum-likelihood formulation via a known simple convex relaxation. We exploit its favorable optimization properties to the full to obtain an approach that: is completely distributed, has a simple implementation at each node, and capitalizes on an optimal gradient method to attain fast convergence. We offer a parallel but also an asynchronous flavor, both with theoretical convergence guarantees and iteration complexity analysis. Experimental results establish leading performance. Our algorithms top the accuracy of a comparable state of the art method by one order of magnitude, using one order of magnitude fewer communications.