论文标题
部分可观测时空混沌系统的无模型预测
Precise Indoor Positioning Based on UWB and Deep Learning
论文作者
论文摘要
我们在这项工作中与指纹技术一起检查了基于UWB的室内位置。我们在UWB室内定位系统的测量距离和实际距离之间建立了连接。该连接用于生成一个可用于生成Fringerprints的距离数据库。我们创建了一个BP神经网络，使用距离数据库将目标节点分类为相关的Fringerpint。与现有的三尾系统相比，我们建议的深度学习技术大大提高了位置的准确性。
We examined UWB-based indoor location in conjunction with a fingerprint technique in this work. We built a connection between the measured and real distances for the UWB indoor positioning system. This connection is used to produce a distance database that may be used to generate fringerprints. We created a BP neural network to classify the target node to the relevant fringerpint using the distance database. Our suggested deep learning technology considerably enhances location accuracy when compared to existing trilateration systems.
