Compared with the line-of-sight (LOS) condition, the multipath effect is more serious in the non-line-of-sight (NLOS) condition. Therefore, the LOS and NLOS identification is necessary for the multipath analysis of signal propagation. The commonly used method is the support vector machine (SVM) method with high computational complexity. To tackle this problem, this paper adopts the SVM classifier based on fewer selected features of the normalized power delay profile (PDP). Therein, the PDP can be obtained using the sliding correlation method. The results show that the SVM-based classifier can achieve high accuracy on LOS and NLOS identification. We then analyze the impact of the signal-to-noise ratio (SNR) and transmitting-receiving (Tx-to-Rx) distance on distinguishable multipaths under LOS and NLOS conditions. According to statistical measurement results, a function of distinguishable multipath numbers is established. Finally, we investigate the multipath power and delay parameters of average delay spread and root mean square (RMS) delay spread based on multipath results. The outcomes of this paper provide a useful support for analyzing signal propagation characteristics.

中文翻译：

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地下隧道中可分辨多径的测量和统计分析

与视线（LOS）条件相比，在非视线（NLOS）条件下多径效应更为严重。因此，LOS和NLOS识别对于信号传播的多路径分析是必要的。常用的方法是计算复杂度高的支持向量机（SVM）方法。为了解决这个问题，本文基于较少的归一化功率延迟曲线（PDP）特征选择了SVM分类器。其中，可以使用滑动相关方法获得PDP。结果表明，基于支持向量机的分类器可以实现LOS和NLOS的高精度识别。然后，我们分析了在LOS和NLOS条件下，信噪比（SNR）和收发比（Tx-Rx）距离对可区分多径的影响。根据统计测量结果，建立可区分的多径数的函数。最后，我们研究了基于多径结果的平均延迟扩展和均方根（RMS）延迟扩展的多路径功率和延迟参数。本文的结果为分析信号传播特性提供了有用的支持。