# Enhanced Indoor Positioning Using RSSI and Time-Distributed Auto Encoder-Gated Recurrent Unit Model

## TL;DR

This paper introduces a new indoor positioning method using RFID and a TAE-GRU model to improve accuracy and speed.

## Contribution

A novel TAE-GRU model combined with Gaussian Kalman filtering for improved indoor positioning accuracy and speed.

## Key findings

- The method achieves a 75.9% improvement in localization accuracy over simple neural networks.
- It significantly reduces localization time, enhancing real-world applicability.
- Temporal relationships in RSSI data are effectively captured by the TAE-GRU model.

## Abstract

This study presents a novel approach to indoor positioning leveraging radio frequency identification (RFID) technology based on received signal strength indication (RSSI). The proposed methodology integrates Gaussian Kalman filtering for effective signal preprocessing and a time-distributed auto encoder-gated recurrent unit (TAE-GRU) model for precise location prediction. Addressing the prevalent challenges of low accuracy and extended localization times in current systems, the proposed method significantly enhances the preprocessing of RSSI data and effectively captures the temporal relationships inherent in the data. Experimental validation demonstrates that the proposed approach achieves a 75.9% improvement in localization accuracy over simple neural network methods and markedly enhances the speed of localization, thereby proving its practical applicability in real-world indoor localization scenarios.