论文标题
机器学习帮助精确的室内定位
Machine Learning aided Precise Indoor Positioning
论文作者
论文摘要
这项研究描述了基于UWB和机器学习（ML）的室内定位系统。我们提出了一个简单的数学策略，以创建数据以减少基于指纹的室内定位系统的测量工作。可以避免使用相当多的测量值。本文比较并对比了四个不同模型的性能。使用最佳模型，大多数测试位置的平均误差可能会降低到小于150 mm。
This study describes a UWB and Machine Learning (ML)-based indoor positioning system. We propose a simple mathematical strategy to create data to reduce the job of measurements for fingerprint-based indoor localization systems. A considerable number of measurements can be avoided this way. The paper compares and contrasts the performance of four distinct models. Most test locations' average error may be reduced to less than 150 mm using the best model.
