发表机构
Del Norte High School; Department of Electrical and Computer Engineering, San Diego State University（德尔诺特高中; 圣地亚哥州立大学电气与计算机工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对商用蓝牙信道探测硬件的方向诱导测距误差，首次采用集成IMU的EFR32xG24平台，通过随机森林模型实现74.6%的MAE降低，验证了IMU辅助校正的有效性。
AI 中文摘要
蓝牙低功耗信道探测（BLE CS）在2024年9月发布的蓝牙核心规范6.0中标准化，可通过基于相位的测距（PBR）和往返时间（RTT）实现距离估计。尽管已有研究探讨了固定方向配置下的CS精度，但尚未有公开研究分析商用硬件上设备方向对测距误差的影响。本文首次研究方向诱导的CS测距误差，并提出利用IMU特征的机器学习校正方法。本研究使用EFR32xG24信道探测开发套件，这是唯一集成六轴惯性测量单元（IMU）的商用CS平台。结果表明，设备方向对CS测距精度有显著影响；在留一方向交叉验证下，基于IMU导出方向训练的随机森林模型实现了74.6%的平均绝对误差（MAE）降低，证明IMU读数具有提升测距精度的潜力。
英文摘要
Bluetooth Low Energy Channel Sounding (BLE CS), standardized in Bluetooth Core Specification 6.0 (September 2024), enables distance estimation via phase-based ranging (PBR) and round-trip time (RTT). Although prior work has studied CS accuracy in configurations with a fixed orientation, no published work has studied how device orientation affects ranging error on commercial hardware. We present the first study of orientation-induced CS ranging error and a Machine Learning correction using IMU features. This study used the EFR32xG24 Channel Sounding Development Kit, the only commercial CS platform with an integrated six-axis Inertial Measurement Unit (IMU). Our results show that device orientation has a substantial effect on CS ranging accuracy; we found that a Random Forest model trained on the IMU derived orientation achieved a 74.6\% Mean Absolute Error (MAE) reduction under a Leave One Orientation Out Evaluation, demonstrating that IMU readings have potential to improve ranging accuracy.
