# Conformal Prediction for Indoor Positioning with Correctness Coverage Guarantees

arXiv: https://arxiv.org/abs/2505.01810 · HTML: https://mlsanity.com/paper/2505.01810/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 3 May 2025.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Addressing Failure Prediction by Learning Model Confidence](https://mlsanity.com/paper/1910.04851/) (2019)
- [Uncertainty Quantification and Deep Ensembles](https://mlsanity.com/paper/2007.08792/) (2020)
- [ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees](https://mlsanity.com/paper/2407.00499/) (2024)
- [SConU: Selective Conformal Uncertainty in Large Language Models](https://mlsanity.com/paper/2504.14154/) (2025)
- [Sample then Identify: A General Framework for Risk Control and Assessment in Multimodal Large Language Models](https://mlsanity.com/paper/2410.08174/) (2024)
- [Word-Sequence Entropy: Towards Uncertainty Estimation in Free-Form Medical Question Answering Applications and Beyond](https://mlsanity.com/paper/2402.14259/) (2024)
- [MetaLoc: Learning to Learn Wireless Localization](https://mlsanity.com/paper/2211.04258/) (2022)
- [Deep Learning Methods for Fingerprint-Based Indoor Positioning: A Review](https://mlsanity.com/paper/2205.14935/) (2022)
- [Positioning Using Wireless Networks: Applications, Recent Progress and Future Challenges](https://mlsanity.com/paper/2403.11417/) (2024)
- [Roadmap on Signal Processing for Next Generation Measurement Systems](https://mlsanity.com/paper/2111.02493/) (2021)
- [Caterpillar: A Pure-MLP Architecture with Shifted-Pillars-Concatenation](https://mlsanity.com/paper/2305.17644/) (2023)

## Built on by (most cited first, 1 of 1)

- [Spatially-Adaptive Conformal Graph Transformer for Indoor Localization in Wi-Fi Driven Networks](https://mlsanity.com/paper/2601.22322/) (2026)

## Abstract (arXiv)

With the advancement of Internet of Things (IoT) technologies, high-precision indoor positioning has become essential for Location-Based Services (LBS) in complex indoor environments. Fingerprint-based localization is popular, but traditional algorithms and deep learning-based methods face challenges such as poor generalization, overfitting, and lack of interpretability. This paper applies conformal prediction (CP) to deep learning-based indoor positioning. CP transforms the uncertainty of the model into a non-conformity score, constructs prediction sets to ensure correctness coverage, and provides statistical guarantees. We also introduce conformal risk control for path navigation tasks to manage the false discovery rate (FDR) and the false negative rate (FNR).The model achieved an accuracy of approximately 100% on the training dataset and 85% on the testing dataset, effectively demonstrating its performance and generalization capability. Furthermore, we also develop a conformal p-value framework to control the proportion of position-error points. Experiments on the UJIIndoLoc dataset using lightweight models such as MobileNetV1, VGG19, MobileNetV2, ResNet50, and EfficientNet show that the conformal prediction technique can effectively approximate the target coverage, and different models have different performance in terms of prediction set size and uncertainty quantification.