# WiCluster: Passive Indoor 2D/3D Positioning using WiFi without Precise Labels

arXiv: https://arxiv.org/abs/2107.01002 · HTML: https://mlsanity.com/paper/2107.01002/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Self-labelling via simultaneous clustering and representation learning](https://mlsanity.com/paper/1911.05371/) (2019)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [SCAN: Learning to Classify Images without Labels](https://mlsanity.com/paper/2005.12320/) (2020)
- [Self-Supervised Learning by Cross-Modal Audio-Video Clustering](https://mlsanity.com/paper/1911.12667/) (2019)
- [TriMap: Large-scale Dimensionality Reduction Using Triplets](https://mlsanity.com/paper/1910.00204/) (2019)
- [Person-in-WiFi: Fine-grained Person Perception using WiFi](https://mlsanity.com/paper/1904.00276/) (2019)
- [Minimum-Distortion Embedding](https://mlsanity.com/paper/2103.02559/) (2021)
- [mD-Track: Leveraging Multi-Dimensionality in Passive Indoor Wi-Fi Tracking](https://mlsanity.com/paper/1812.03103/) (2018)

## Built on by (most cited first, 4 of 4)

- [Benchmarking Learnt Radio Localisation under Distribution Shift](https://mlsanity.com/paper/2210.01930/) (2022)
- [Neural 5G Indoor Localization with IMU Supervision](https://mlsanity.com/paper/2402.09948/) (2024)
- [Lessons from Deploying Learning-based CSI Localization on a Large-Scale ISAC Platform](https://mlsanity.com/paper/2504.17173/) (2025)
- [Self-Localizing MIMO Beam Mapping with Continuously Evolving Channel Memory](https://mlsanity.com/paper/2511.17007/) (2025)

## Abstract (arXiv)

We introduce WiCluster, a new machine learning (ML) approach for passive indoor positioning using radio frequency (RF) channel state information (CSI). WiCluster can predict both a zone-level position and a precise 2D or 3D position, without using any precise position labels during training. Prior CSI-based indoor positioning work has relied on non-parametric approaches using digital signal-processing (DSP) and, more recently, parametric approaches (e.g., fully supervised ML methods). However these do not handle the complexity of real-world environments well and do not meet requirements for large-scale commercial deployments: the accuracy of DSP-based method deteriorates significantly in non-line-of-sight conditions, while supervised ML methods need large amounts of hard-to-acquire centimeter accuracy position labels. In contrast, WiCluster is precise, requires weaker label-information that can be easily collected, and works well in non-line-of-sight conditions. Our first contribution is a novel dimensionality reduction method for charting. It combines a triplet-loss with a multi-scale clustering-loss to map the high-dimensional CSI representation to a 2D/3D latent space. Our second contribution is two weakly supervised losses that map this latent space into a Cartesian map, resulting in meter-accuracy position results. These losses only require simple to acquire priors: a sketch of the floorplan, approximate access-point locations and a few CSI packets that are labelled with the corresponding zone in the floorplan. Thirdly, we report results and a robustness study for 2D positioning in two single-floor office buildings and 3D positioning in a two-story home.