A key enabler for meeting the stringent requirements of 6G positioning is the ability to exploit site-dependent information governing line-of-sight (LoS) and non-line-of-sight (NLoS) propagation. However, acquiring such environmental information in real time is challenging in practice. To address this issue, we propose a novel NLoS detection algorithm termed combinatorial data augmentation-guided NLoS detection (CDA-ND), which builds upon our prior work. CDA-ND generates numerous preliminary estimated locations (PELs) by applying multilateration over many gNodeB (gNB) combinations using a single snapshot of range measurements. When a target gNB is in NLoS, the resulting PELs split into two clusters: one derived using the target gNB's range measurement and the other derived without it. Their displacement is summarized by a single vector, called the NLoS evidence vector (NEV), which is used to compute an NLoS likelihood score. Based on this score, two modes of NLoS detection are developed. First, each gNB is classified as LoS or NLoS, termed hard decision (HD), using a simple threshold test. Second, each gNB's NLoS confidence is probabilistically quantified, termed soft decision (SD), which extends HD with weak site-survey priors, namely empirical NLoS-score samples and the average NLoS probability. We then design positioning algorithms tailored to these two modes by excluding gNBs deemed NLoS and re-weighting the remaining gNBs for SD. The proposed CDA-ND achieves high reliability in indoor factory environments under frequency range 1, attaining NLoS detection accuracies of 96.6% and 91.1% when the proportion of NLoS gNBs is approximately 18% and 56%, respectively. As a result, integrating CDA-ND into positioning significantly reduces mean absolute error by 20.04% and 65.99% in LoS- and NLoS-dominant environments, respectively.

翻译：满足6G定位严苛要求的关键在于利用站点相关的视距与非视距传播信息。然而，实时获取此类环境信息在实践中颇具挑战。为此，我们提出一种称为组合数据增强引导的非视距检测算法，该算法基于我们先前的研究工作。该算法利用单次距离测量快照，通过多个gNodeB组合的多边定位生成大量初步估计位置。当目标gNodeB处于非视距状态时，所得初步估计位置会分裂为两个聚类：一个使用目标gNodeB的距离测量值生成，另一个则不使用该测量值。这两个聚类之间的位移可通过称为非视距证据向量的单个向量进行表征，并用于计算非视距似然分数。基于该分数，我们开发了两种非视距检测模式：首先，通过简单的阈值测试将每个gNodeB分类为视距或非视距，称为硬决策；其次，对每个gNodeB的非视距置信度进行概率量化，称为软决策，该模式通过引入弱站点勘验先验信息（即经验非视距分数样本和平均非视距概率）扩展了硬决策。随后，我们针对这两种模式设计了定制化的定位算法：对于硬决策排除被判定为非视距的gNodeB，对于软决策则对剩余gNodeB进行重新加权。所提出的算法在频段1的室内工厂环境中实现了高可靠性，当非视距gNodeB比例约为18%和56%时，非视距检测准确率分别达到96.6%和91.1%。因此，将该算法集成到定位系统中，在视距主导和非视距主导环境下分别使平均绝对误差显著降低了20.04%和65.99%。