Machine Learning Models for Indoor Positioning Using Bluetooth RSSI and Video Data: A Case Study

TL;DR

This paper presents a new indoor positioning system that combines Bluetooth signals and video data to improve accuracy in challenging environments.

Contribution

The novel contribution is a multimodal indoor positioning system using Bluetooth RSSI and video data with ensemble learning.

Findings

Ensemble models outperformed individual RSSI and video-based models in positioning accuracy.

Multimodal data improved performance despite constraints like multipath interference and limited beacon infrastructure.

Abstract

Indoor Positioning Systems (IPSs) are essential for applications requiring accurate location awareness in indoor environments. However, achieving high precision remains challenging due to signal interference and environmental variability. This study proposes a multimodal IPS that integrates Bluetooth Received Signal Strength Indicator (RSSI) measurements and video imagery using machine learning (ML) and ensemble learning techniques. The system was implemented and deployed in the Hall of Biodiversity at the Natural History and Science Museum of the University of Porto. The venue presented significant deployment issues, namely restrictions on beacon placement and lighting conditions. We trained independent ML models on RSSI and video datasets, and combined them through ensemble learning methods. The experimental results from test scenarios, which included simulated visitor trajectories, showed that ensemble models consistently outperformed the RSSI-based and video-based models. These findings demonstrate that the use of multimodal data can significantly improve IPS accuracy despite constraints such as multipath interference, low lighting, and limited beacon infrastructure. Overall, they highlight the potential of multimodal data for deployments in complex indoor environments.

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