# Recurrent Neural Networks For Accurate RSSI Indoor Localization

## TL;DR

This paper introduces RNN-based methods for indoor WiFi fingerprinting localization, leveraging trajectory data and a weighted filter to improve accuracy over traditional algorithms.

## Contribution

It presents a novel RNN framework for trajectory-based indoor localization and a weighted averaging filter to enhance RSSI and position accuracy.

## Key findings

- Achieves an average error of 0.75 m in on-site tests.
- Outperforms KNN and probabilistic algorithms by about 30%.
- 80% of errors are under 1 meter.

## Abstract

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.