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UWB anchor nodes self-calibration in NLOS conditions: a machine learning and adaptive PHY error correction approach
Wireless Networks ( IF 3 ) Pub Date : 2021-05-07 , DOI: 10.1007/s11276-021-02631-0
Matteo Ridolfi , Jaron Fontaine , Ben Van Herbruggen , Wout Joseph , Jeroen Hoebeke , Eli De Poorter

Ultra-wideband (UWB) positioning performance is highly related to the accuracy of the coordinates of the fixed anchor nodes, which form the system infrastructure. The process of determining the position of the anchors is called calibration. In an anchor-based system, it is crucial for the fixed nodes to know their locations with the highest possible accuracy. However, in certain situations, it is almost impossible to perform the calibration manually, e.g., during emergency interventions. Moreover, calibration is always delicate and time-consuming. We designed an effortless and accurate self-calibration algorithm that does not require any manual intervention to precisely pinpoint the position of the anchors. This paper presents an innovative algorithm that combines machine learning and exploits the time resolution capabilities of UWB with adaptive physical settings to enable the automatic calibration of the fixed anchor nodes, even in realistic NLOS (non-line-of-sight) conditions. The self-calibration algorithm combines iterative gradient descent to pinpoint the positions of the anchors and uses error detection and correction from a convolutional neural network. Moreover, the algorithm can use a different set of settings for each anchor pair. This is done to ensure the most robust and accurate communication between nodes. Extensive measurements were carried out to allow anchors to estimate distances among each others. Distances were then combined and processed by the self-calibration algorithm. Experimental evaluation in two complex and large environments with many obstacles and reflections shows that accuracy reached by the algorithm is about 2.4 cm on average and 95th percentile is 5.7 cm, in best case. The results refer to the relative positions among the anchors. Results prove that in order to precisely calibrate the anchors nodes in an UWB positioning system, high correctness can be obtained by combining the accuracy of UWB together with deep learning and adaptive PHY modulation schemes.



中文翻译:

NLOS条件下的UWB锚节点自校准:一种机器学习和自适应PHY纠错方法

超宽带(UWB)的定位性能与构成系统基础结构的固定锚点的坐标的准确性高度相关。确定锚点位置的过程称为校准。在基于锚的系统中,至关重要的是,固定节点必须以尽可能最高的准确度知道它们的位置。但是,在某些情况下,几乎不可能手动执行校准,例如在紧急干预期间。而且,校准始终是微妙且耗时的。我们设计了一种毫不费力且精确的自校准算法,该算法不需要任何手动干预即可精确地确定锚点的位置。本文提出了一种创新的算法,该算法结合了机器学习功能,并利用超宽带的时间分辨率功能与自适应物理设置来实现对固定锚节点的自动校准,即使在现实的NLOS(非视距)条件下也是如此。自校准算法结合了迭代梯度下降来精确定位锚点的位置,并使用卷积神经网络中的错误检测和校正功能。此外,该算法可以为每个锚对使用不同的设置集。这样做是为了确保节点之间的最健壮和准确的通信。进行了广泛的测量,以允许锚点之间的距离进行估计。然后将距离合并并通过自校准算法进行处理。在两个有很多障碍和反射的复杂大环境中的实验评估表明,在最佳情况下,该算法所达到的准确度平均约为2.4厘米,而第95个百分位数为5.7厘米。结果指的是锚之间的相对位置。结果证明,为了精确地校准UWB定位系统中的锚节点,可以通过将UWB的精度与深度学习和自适应PHY调制方案相结合来获得较高的正确性。

更新日期:2021-05-07
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