论文标题
使用UWB技术基于信号强度的距离估计
On signal strength-based distance estimation using UWB technology
论文作者
论文摘要
由于使用基于飞行时间的技术，因此超宽带（UWB）技术在室内定位和距离估计（DE）系统中已经非常流行。 DE依赖信号强度（DESS）的技术受到较少的关注。结果，现有的基准是由简单的频道表征组成的，而不是旨在提高准确性的方法。 DESS的进一步发展可能使比基于飞行时间的应用程序能够负担得起精度较低的应用程序。此外，这是最近提出的方法使用的基本构件，可以使DE上的网络攻击具有安全性，仅使用基于飞行时间的技术就无法避免。在本文中，我们评估了在不同现实世界环境中训练的几种机器学习模型的适用性，以提高基于UWB的DESS精度。此外，为了在商业现成（COTS）收发器中实施实施，我们提出并评估一种解决这些设备中包括DESS的歧义的方法。我们的结果表明，在对其训练的相同环境和位置测试模型时，提出的DE方法具有次数的精度，并在不同环境中进行测试时达到了24厘米的平均MAE。从我们的实验中获得的3个数据集可公开使用。
Ultra-wideband (UWB) technology has become very popular for indoor positioning and distance estimation (DE) systems due to its decimeter-level accuracy achieved when using time-of-flight-based techniques. Techniques for DE relying on signal strength (DESS) received less attention. As a consequence, existing benchmarks consist of simple channel characterizations rather than methods aiming to increase accuracy. Further development in DESS may enable lower-cost transceivers to applications that can afford lower accuracies than those based on time-of-flight. Moreover, it is a fundamental building block used by a recently proposed approach that can enable security against cyberattacks on DE which could not be avoided using only time-of-flight-based techniques. In this paper, we evaluate the suitability of several machine-learning models trained in different real-world environments to increase UWB-based DESS accuracy. Additionally, aiming for implementation in commercial off-the-shelf (COTS) transceivers, we propose and evaluate an approach to resolve ambiguities comprising DESS in these devices. Our results show that the proposed DE approaches have sub-decimeter accuracy when testing the models in the same environment and positions in which they have been trained, and achieved an average MAE of 24 cm when tested in a different environment. 3 datasets obtained from our experiments are made publicly available.
