# Supervised and Semi-supervised Deep Probabilistic Models for Indoor Positioning Problems

arXiv: https://arxiv.org/abs/1911.09906 · HTML: https://mlsanity.com/paper/1911.09906/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2019.

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

- [IndoorGNN: A Graph Neural Network based approach for Indoor Localization using WiFi RSSI](https://mlsanity.com/paper/2312.07609/) (2023)
- [Variational Information Bottleneck Model for Accurate Indoor Position Recognition](https://mlsanity.com/paper/2101.10655/) (2021)
- [Generative AI Meets Wireless Sensing: Towards Wireless Foundation Model](https://mlsanity.com/paper/2509.15258/) (2025)
- [Radiance-Field Guided Pretraining: Scaling Localization Models with Unlabeled Wireless Signals](https://mlsanity.com/paper/2512.07309/) (2025)

## Abstract (arXiv)

Predicting smartphone users location with WiFi fingerprints has been a popular research topic recently. In this work, we propose two novel deep learning-based models, the convolutional mixture density recurrent neural network and the VAE-based semi-supervised learning model. The convolutional mixture density recurrent neural network is designed for path prediction, in which the advantages of convolutional neural networks, recurrent neural networks and mixture density networks are combined. Further, since most of real-world datasets are not labeled, we devise the VAE-based model for the semi-supervised learning tasks. In order to test the proposed models, we conduct the validation experiments on the real-world datasets. The final results verify the effectiveness of our approaches and show the superiority over other existing methods.