The error compensation and suppression effects of traditional ultra-wideband (UWB) ranging in non-line-of-sight (NLOS) environments are limited. The contribution of specific occlusions to UWB spatial perception in NLOS is ignored. To achieve a comprehensive sensing of spatial information by UWB, we initially analyze the channel impulse response (CIR) and the underlying parameters of registers during UWB communication. By comparing the difference between NLOS and line-of-sight (LOS) environments for each parameter on a continuous time series, a fast discriminative method for UWB environment conversion is proposed. Further, combining the ensemble learning XGBoost classifier, an efficient NLOS occlusion recognition method is proposed. At the same time, an algorithm optimization based on discrete degree threshold is designed. It is based on the loss function probability matrix weighted predictive labeling. The prediction matrix of the loss function of the XGBoost algorithm is used as label weights. The UWB prediction labels of continuous time series are weighted, which mitigates the effect of low probability data on the overall prediction results. Finally, UWB spatial sensing experiments are carried out to verify the reliability of the proposed method. The experimental results show that the mutation of the parameter profile can effectively perceive the LOS/NLOS transition. The recognition accuracy of the proposed occlusion recognition method in condition of human, metal occlusion and wall occlusion are 94.44 %, 92.00 % and 95.87 %, respectively. In contrast to the origin method, the suggested algorithm’s average recognition accuracy has increased by 16.71%. Its precise recognition accuracy makes UWB spatial sensing more effective.

中文翻译：

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改善 UWB 空间感知的 NLOS 遮挡识别方法


传统超宽带(UWB)测距在非视距(NLOS)环境下的误差补偿和抑制效果有限。 NLOS 中特定遮挡对 UWB 空间感知的贡献被忽略。为了实现UWB对空间信息的全面感知，我们首先分析了UWB通信过程中的信道脉冲响应（CIR）和寄存器的底层参数。通过比较连续时间序列上每个参数的 NLOS 和视距 (LOS) 环境之间的差异，提出了一种 UWB 环境转换的快速判别方法。进一步，结合集成学习XGBoost分类器，提出了一种高效的NLOS遮挡识别方法。同时设计了基于离散度阈值的算法优化。它基于损失函数概率矩阵加权预测标记。使用XGBoost算法的损失函数的预测矩阵作为标签权重。对连续时间序列的UWB预测标签进行加权，减轻低概率数据对整体预测结果的影响。最后，进行了UWB空间感知实验，验证了所提方法的可靠性。实验结果表明，参数轮廓的突变可以有效感知LOS/NLOS转变。所提出的遮挡识别方法在人体、金属遮挡和墙壁遮挡情况下的识别准确率分别为94.44%、92.00%和95.87%。与origin方法相比，建议算法的平均识别准确率提高了16.71%。其精确的识别精度使UWB空间感知更加有效。