# Wireless localization with diffusion maps

arXiv: https://arxiv.org/abs/1908.05216 · HTML: https://mlsanity.com/paper/1908.05216/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2019.

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

- [Diffusion Maps for Signal Filtering in Graph Learning](https://mlsanity.com/paper/2312.14758/) (2023)
- [Nonlinear Dimensionality Reduction with Diffusion Maps in Practice](https://mlsanity.com/paper/2601.20428/) (2026)

## Abstract (arXiv)

In the Wireless Localization Matching Problem (WLMP) the challenge is to match pieces of equipment with a set of candidate locations based on wireless signal measurements taken by the pieces of equipment. This challenge is complicated by the noise that is inherent in wireless signal measurements. Here we propose the use of diffusion maps, a manifold learning technique, to obtain an embedding of positions and equipment coordinates in a space that enables coordinate comparison and reliable evaluation of assignment quality at very low computational cost. We show that the mapping is robust to noise and using diffusion maps allows for accurate matching in a realistic setting. This suggests that the diffusion-map-based approach could significantly increase the accuracy of wireless localization in applications.