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Bluetooth Low Energy Dataset Using Separate-Channel Fingerprinting Techniques and Frequency Scanned Antennas.

José Antonio López-Pastor¹, Alejandro Gil-Martinez², Antonio Hernández-Mateos²

¹Department of Engineering and Applied Techniques, Universitary Defense Center, San Javier Air Base, Murcia, Spain. joseantonio.lopez@cud.upct.es.

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|February 12, 2025

Summary

This study introduces a new dataset for Separate Channel Fingerprinting (SCFP) using Bluetooth Low Energy (BLE) and Frequency Scanned Leaky Wave Antennas (FSLWA). This approach improves indoor real-time location systems (IRTLS) accuracy by analyzing individual BLE channels.

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Area of Science:

Wireless communication
Indoor positioning systems
Signal processing

Background:

Indoor real-time location systems (IRTLS) commonly use Bluetooth Low Energy (BLE) fingerprinting with Received Signal Strength Indicator (RSSI).
Existing datasets often employ Unified Channel Fingerprinting (UCFP), collecting RSSI from all three BLE advertising channels without differentiation.
This undifferentiated approach may limit the accuracy and robustness of indoor location systems.

Purpose of the Study:

To introduce and release a novel dataset for Separate Channel Fingerprinting (SCFP) in BLE-based indoor positioning.
To evaluate the performance of SCFP utilizing Frequency Scanned Leaky Wave Antennas (FSLWA) against traditional UCFP.
To provide a robust dataset for advancing the accuracy of indoor real-time location systems.

Main Methods:

Development of two distinct data collection sub-systems: one with monopole antennas and another with FSLWAs.
Deployment of four BLE dongles across a 35m² indoor area for data acquisition.
Sequential data collection over 94 days, incorporating environmental obstacles to test system robustness.

Main Results:

The study presents a unique dataset enabling the comparison of SCFP with FSLWA against UCFP.
The collected data includes calibration and test measurements under varying environmental conditions.
The dataset is designed to assess the resilience of SCFP, particularly when integrated with FSLWA technology.

Conclusions:

The newly released SCFP dataset offers a valuable resource for researchers in indoor positioning.
The integration of FSLWA with SCFP shows promise for enhancing the robustness of BLE-based location systems.
This work contributes to the development of more accurate and reliable indoor real-time location systems.