# Hybrid Building/Floor Classification and Location Coordinates Regression Using A Single-Input and Multi-Output Deep Neural Network for Large-Scale Indoor Localization Based on Wi-Fi Fingerprinting

arXiv: https://arxiv.org/abs/1810.05868 · HTML: https://mlsanity.com/paper/1810.05868/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2018.

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

- [Hierarchical Multi-Building And Multi-Floor Indoor Localization Based On Recurrent Neural Networks](https://mlsanity.com/paper/2112.12478/) (2021)
- [Hierarchical Stage-Wise Training of Linked Deep Neural Networks for Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi RSSI Fingerprinting](https://mlsanity.com/paper/2407.13288/) (2024)
- [Mean Teacher based SSL Framework for Indoor Localization Using Wi-Fi RSSI Fingerprinting](https://mlsanity.com/paper/2407.13303/) (2024)

## Abstract (arXiv)

In this paper, we propose hybrid building/floor classification and floor-level two-dimensional location coordinates regression using a single-input and multi-output (SIMO) deep neural network (DNN) for large-scale indoor localization based on Wi-Fi fingerprinting. The proposed scheme exploits the different nature of the estimation of building/floor and floor-level location coordinates and uses a different estimation framework for each task with a dedicated output and hidden layers enabled by SIMO DNN architecture. We carry out preliminary evaluation of the performance of the hybrid floor classification and floor-level two-dimensional location coordinates regression using new Wi-Fi crowdsourced fingerprinting datasets provided by Tampere University of Technology (TUT), Finland, covering a single building with five floors. Experimental results demonstrate that the proposed SIMO-DNN-based hybrid classification/regression scheme outperforms existing schemes in terms of both floor detection rate and mean positioning errors.