基于四参数Kappa分布的L矩 LOS和NLOS信道特性表征用于AoA BLE CTE测量
L-Moment-Based LOS and NLOS Channel Characterization via Four-parameter Kappa Distribution for AoA BLE CTE Measurements
AI总结：
本文提出基于四参数Kappa分布的L矩方法，用于改进BLE CTE测量中LOS和NLOS信道特性的表征与分离。
摘要：
蓝牙低功耗（BLE）CTE传输提供相位和正交（IQ）样本，其经验统计特性强烈受传播环境影响。特别是，LOS和NLOS条件下的分布差异显著。在NLOS条件下，多径引起的失真通常会降低AoA估计的准确性。现有BLE方向定位数据集很少提供紧密控制的IQ级配对LOS和NLOS测量，并且通常使用的平坦衰落模型可能无法满足具有重尾功率分布的室内复杂环境。为了解决这些限制，我们使用现成模块进行配对几何BLE AoA测量活动，收集了132000个标记的CTE数据包，在匹配的锚点-标签条件下。一个稳健的预处理阶段使用组合的单变量和多变量标准去除异常CTE。对IQ衍生的功率特征进行特征级假设检验，确认了强LOS和NLOS分离性。所有均值差异在统计上显著；此外，92%的特征级方差差异也显著。我们进一步计算L矩比（LMRs）并分析其在L矩比图（LMRD）中的表现，显示NLOS子集表现出明显更重的尾部和更强的不对称性。从LMRs拟合的Kappa族分布提供了显著改进的双评分L-矩拟合优度（GoF）。具体而言，对于NLOS，其在LMRD中的最小差异和接近零的标准化L峰度偏差。作为实践，我们应用自监督聚类到L矩统计学，相比产品矩，实现了更可分离的表示。
英文摘要：
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.
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