The paper describes an NLOS (Non-Line-of-Sight) mitigation method intended
for use in a UWB positioning system. In the proposed method propagation
conditions between the localized objects and the anchors forming system
infrastructure are classified into one of three categories: LOS
(Line-of-Sight), NLOS and severe NLOS. Non-Line-of-Sight detection is conducted
based on first path signal component power measurements. For each of the
categories, average NLOS inducted time of arrival bias and bias standard
deviation have been estimated based on results gathered during a measurement
campaign conducted in a fully furnished apartment. To locate a tag, an EKF
(Extended Kalman Filter) based algorithm is used. The proposed method of NLOS
mitigation consists in correcting measurement results obtained in NLOS
conditions and lowering their significance in a tag position estimation
process. The paper includes the description of the method and the results of
the conducted experiments.

该论文描述了一种用于 UWB 定位系统的 NLOS（非直射视线）抑制方法，该方法将定位对象与构成系统基础设施的锚定点之间的传播条件分类为 LOS（直射视线）、NLOS 和严重 NLOS。通过首次信号成分功率测量进行非直射视线检测，并据此估计每个类别的平均 NLOS 到达时间偏差和偏差标准差。使用基于扩展卡尔曼滤波器（EKF）的算法来定位标签，NLOS 抑制方法通过纠正在 NLOS 条件下获得的测量结果并降低其在标签位置估计过程中的重要性。该论文包括了该方法的描述以及进行的实验结果。

基于首个路径组件功率的非直视传播抑制技术在 UWB 定位系统中的应用

First path component power based NLOS mitigation in UWB positioning  system

Radio frequency (RF)-based techniques are widely adopted for indoor
localization despite the challenges in extracting sufficient information from
measurements. Soft range information (SRI) offers a promising alternative for
highly accurate localization that gives all probable range values rather than a
single estimate of distance. We propose a deep learning approach to generate
accurate SRI from RF measurements. In particular, the proposed approach is
implemented by a network with two neural modules and conducts the generation
directly from raw data. Extensive experiments on a case study with two public
datasets are conducted to quantify the efficiency in different indoor
localization tasks. The results show that the proposed approach can generate
highly accurate SRI, and significantly outperforms conventional techniques in
both non-line-of-sight (NLOS) detection and ranging error mitigation.

本文提出了一种基于深度学习的方法来从射频测量数据中生成准确的软量信息（SRI），实现不同户内定位任务中的非直线视距检测和距离误差缓解，并在两种公共数据集的案例研究中进行了广泛的实验以量化效率。