Enhancing Bluetooth Channel Sounding Performance in Complex Indoor Environments

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 $\text{90}{\%}$ peak error of $\leq$ $\text{1.6} \,\text{m}$ without data-dependent adaptation and $\leq$ $\text{1.2} \,\text{m}$ 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.