# Towards Ubiquitous Indoor Positioning: Comparing Systems across Heterogeneous Datasets

arXiv: https://arxiv.org/abs/2109.09436 · HTML: https://mlsanity.com/paper/2109.09436/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 20 Sep 2021.

## Builds on (most cited first, 3 of 3)

- [Machine Learning Testing: Survey, Landscapes and Horizons](https://mlsanity.com/paper/1906.10742/) (2019)
- [PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison](https://mlsanity.com/paper/1703.00512/) (2017)
- [Accurate Real Time Localization Tracking in A Clinical Environment using Bluetooth Low Energy and Deep Learning](https://mlsanity.com/paper/1711.08149/) (2017)

## Built on by (most cited first, 3 of 3)

- [LLM-Guided Indoor Navigation with Multimodal Map Understanding](https://mlsanity.com/paper/2503.11702/) (2025)
- [SURIMI: Supervised Radio Map Augmentation with Deep Learning and a Generative Adversarial Network for Fingerprint-based Indoor Positioning](https://mlsanity.com/paper/2207.06120/) (2022)
- [Tracking daily paths in home contexts with RSSI fingerprinting based on UWB through deep learning models](https://mlsanity.com/paper/2509.06161/) (2025)

## Abstract (arXiv)

The evaluation of Indoor Positioning Systems (IPS) mostly relies on local deployments in the researchers' or partners' facilities. The complexity of preparing comprehensive experiments, collecting data, and considering multiple scenarios usually limits the evaluation area and, therefore, the assessment of the proposed systems. The requirements and features of controlled experiments cannot be generalized since the use of the same sensors or anchors density cannot be guaranteed. The dawn of datasets is pushing IPS evaluation to a similar level as machine-learning models, where new proposals are evaluated over many heterogeneous datasets. This paper proposes a way to evaluate IPSs in multiple scenarios, that is validated with three use cases. The results prove that the proposed aggregation of the evaluation metric values is a useful tool for high-level comparison of IPSs.