Bluetooth Low Energy (BLE) CTE transmissions provide in-phase and quadrature (IQ) samples whose empirical statistics are strongly governed by the propagation regime. in particular, the distributions differ markedly between line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. In NLOS, multipath-induced distortions typically degrade Angle-of-Arrivial (AoA) estimation accuracy. Existing BLE direction finding datasets rarely provide tightly controlled, IQ-level paired LOS and NLOS measurements with rigorous statistical validation, and commonly used flat-fading models can be inadequate for cluttered indoor environments exhibiting heavy-tailed power distributions. To address these limitations, we conduct a paired-geometry BLE AoA measurement campaign using an off-the-shelf module, collecting 132000 labeled CTE packets under matched anchor-tag conditions. A robust preprocessing stage removes anomalous CTEs using combined univariate and multivariate criteria. Feature-wise hypothesis tests on IQ-derived power features confirm strong LOS and NLOS separability. All mean differences are statistically significant; additionally, 92 percent of feature-wise variance differences are significant. We further compute L-moment ratios (LMRs) and analyze them in the L-moment Ratio Diagram (LMRD), showing that NLOS subsets exhibit markedly heavier tails and stronger asymmetry than LOS. Kappa-family distributions fitted from LMRs provide substantially improved dual scored L--moment goodness-of-fit (GoF), Specifically, for NLOS, which is the smallest discrepancy in the LMRD and a near-zero standardized L-kurtosis deviation. As a practice, we apply a self-supervised clustering to L-moment statistics, achieving a more separable representation, compared to product moments.

翻译：蓝牙低功耗（BLE）CTE传输提供的同相与正交（IQ）样本，其经验统计特性主要受传播机制支配。特别是在视距（LOS）与非视距（NLOS）条件下，分布差异显著。在NLOS环境中，多径效应引起的失真通常会降低到达角（AoA）估计的精度。现有的BLE测向数据集很少提供严格受控的、IQ层级配对的LOS与NLOS测量结果并进行严谨的统计验证，且常用的平坦衰落模型对于呈现重尾功率分布的杂乱室内环境可能并不适用。为应对这些局限，我们使用现成模块开展了一项配对几何结构的BLE AoA测量活动，在匹配的锚点-标签条件下收集了132,000个带标签的CTE数据包。通过结合单变量与多变量准则的鲁棒预处理阶段，剔除了异常CTE数据。对IQ衍生的功率特征进行的逐特征假设检验，证实了LOS与NLOS之间存在强可分离性。所有均值差异均具有统计显著性；此外，92%的逐特征方差差异也显著。我们进一步计算了L矩比（LMRs），并在L矩比图（LMRD）中对其进行分析，结果表明NLOS子集比LOS表现出明显更重的尾部与更强的不对称性。基于LMRs拟合的Kappa族分布显著改善了双评分L矩拟合优度（GoF），具体而言，对于NLOS，其在LMRD中的差异最小，且标准化L峰度偏差接近于零。作为实践应用，我们将自监督聚类应用于L矩统计量，与乘积矩相比，获得了更具可分离性的表征。