# Recurrent Neural Networks For Accurate RSSI Indoor Localization

arXiv: https://arxiv.org/abs/1903.11703 · HTML: https://mlsanity.com/paper/1903.11703/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2019.

## Built on by (most cited first, 15 of 15)

- [Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications](https://mlsanity.com/paper/2207.00300/) (2022)
- [An Overview of Machine Learning Techniques for Radiowave Propagation Modeling](https://mlsanity.com/paper/2101.11760/) (2021)
- [Deep Learning Methods for Fingerprint-Based Indoor Positioning: A Review](https://mlsanity.com/paper/2205.14935/) (2022)
- [Multi-Modal Recurrent Fusion for Indoor Localization](https://mlsanity.com/paper/2203.00510/) (2022)
- [Variational Information Bottleneck Model for Accurate Indoor Position Recognition](https://mlsanity.com/paper/2101.10655/) (2021)
- [Radio-Assisted Human Detection](https://mlsanity.com/paper/2112.08743/) (2021)
- [Multi-Band Wi-Fi Sensing with Matched Feature Granularity](https://mlsanity.com/paper/2112.14006/) (2021)
- [A Simplistic and Cost-Effective Design for Real-World Development of an Ambient Assisted Living System for Fall Detection and Indoor Localization: Proof of Concept](https://mlsanity.com/paper/2207.11623/) (2022)
- [OpenCSI: An Open-Source Dataset for Indoor Localization Using CSI-Based Fingerprinting](https://mlsanity.com/paper/2104.07963/) (2021)
- [A Kernel Method to Nonlinear Location Estimation with RSS-based Fingerprint](https://mlsanity.com/paper/2204.03724/) (2022)
- [Indoor Positioning via Gradient Boosting Enhanced with Feature Augmentation using Deep Learning](https://mlsanity.com/paper/2211.08752/) (2022)
- [Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification](https://mlsanity.com/paper/2204.10418/) (2022)
- [Contactless Human Activity Recognition using Deep Learning with Flexible and Scalable Software Define Radio](https://mlsanity.com/paper/2304.09756/) (2023)
- [Material Identification Via RFID For Smart Shopping](https://mlsanity.com/paper/2504.17898/) (2025)
- [Permutation-Invariant Transformer Neural Architectures for Set-Based Indoor Localization Using Learned RSSI Embeddings](https://mlsanity.com/paper/2506.00656/) (2025)

## Abstract (arXiv)

This paper proposes recurrent neuron networks (RNNs) for a fingerprinting indoor localization using WiFi. Instead of locating user's position one at a time as in the cases of conventional algorithms, our RNN solution aims at trajectory positioning and takes into account the relation among the received signal strength indicator (RSSI) measurements in a trajectory. Furthermore, a weighted average filter is proposed for both input RSSI data and sequential output locations to enhance the accuracy among the temporal fluctuations of RSSI. The results using different types of RNN including vanilla RNN, long short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are presented. On-site experiments demonstrate that the proposed structure achieves an average localization error of 0.75 m with 80% of the errors under 1 m, which outperforms the conventional KNN algorithms and probabilistic algorithms by approximately 30% under the same test environment.