Enhancing Bluetooth Channel Sounding Performance in Complex Indoor Environments

The Internet of Things (IoT) relies on accurate distance estimation between devices, crucial for localization in various applications. While received signal strength indicator (RSSI)-based ranging lacks precision and time-of-flight narrow band systems perform poorly, phase-based ranging emerges as the preferred choice for Bluetooth Low Energy (BLE). Infineon's BLE prototype and its performance with a novel processing pipeline based on the minimum variance distortionless response (MVDR) algorithm are presented in this letter. Our pipeline comprises subalgorithms for preprocessing, scene identification, feature selection, feature engineering, and postprocessing. Preprocessing includes zero distance calibration, low-pass filtering, and time history averaging. Scene identification adapts parameters to environmental conditions. MVDR algorithms enable high-resolution feature transformation to project the residual phase correction term to the range domain. Postprocessing includes a tracker and data-dependent adaptation. Postprocessing in conjunction with feature selection tracks the line of sight path, minimizing distance jitter. Our proposed pipeline achieves a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{90}{\%}$</tex-math></inline-formula> peak error of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\leq$</tex-math></inline-formula><inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{1.6} \,\text{m}$</tex-math></inline-formula> without data-dependent adaptation and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\leq$</tex-math></inline-formula><inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{1.2} \,\text{m}$</tex-math></inline-formula> with data-dependent adaptation and tracking, outperforming existing methods in the literature. This work demonstrates the potential of Infineon's BLE channel sounding for accurate range estimation in IoT applications.

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Publication Details

Journal
IEEE Sensors Letters
Published
2024-09-09
DOI
https://doi.org/10.1109/lsens.2024.3456002
Citations
12
Primary Topic
Bluetooth and Wireless Communication Technologies
Type
article
Field-Weighted Citation Impact
4.19
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article

Enhancing Bluetooth Channel Sounding Performance in Complex Indoor Environments

Igor Kravets, Avik Santra, Nazarii Kotliar, Ashutosh Pandey
12 citations
IEEE Sensors Letters
Bluetooth and Wireless Communication Technologies
4.19
article

Enhancing Bluetooth Channel Sounding Performance in Complex Indoor Environments

Igor Kravets, Avik Santra, Nazarii Kotliar, Ashutosh Pandey
article en
12 citations

Abstract

The Internet of Things (IoT) relies on accurate distance estimation between devices, crucial for localization in various applications. While received signal strength indicator (RSSI)-based ranging lacks precision and time-of-flight narrow band systems perform poorly, phase-based ranging emerges as the preferred choice for Bluetooth Low Energy (BLE). Infineon's BLE prototype and its performance with a novel processing pipeline based on the minimum variance distortionless response (MVDR) algorithm are presented in this letter. Our pipeline comprises subalgorithms for preprocessing, scene identification, feature selection, feature engineering, and postprocessing. Preprocessing includes zero distance calibration, low-pass filtering, and time history averaging. Scene identification adapts parameters to environmental conditions. MVDR algorithms enable high-resolution feature transformation to project the residual phase correction term to the range domain. Postprocessing includes a tracker and data-dependent adaptation. Postprocessing in conjunction with feature selection tracks the line of sight path, minimizing distance jitter. Our proposed pipeline achieves a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{90}{\%}$</tex-math></inline-formula> peak error of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\leq$</tex-math></inline-formula><inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{1.6} \,\text{m}$</tex-math></inline-formula> without data-dependent adaptation and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\leq$</tex-math></inline-formula><inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{1.2} \,\text{m}$</tex-math></inline-formula> with data-dependent adaptation and tracking, outperforming existing methods in the literature. This work demonstrates the potential of Infineon's BLE channel sounding for accurate range estimation in IoT applications.

IEEE Sensors LettersVol. 8(10)
Lviv University (UA), Infineon Technologies (United States) (US)
Openalex Percentile: Top 5%
Bluetooth and Wireless Communication Technologies
4.19
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