College of Electronic Engineering, National University of Defense Technology, Hefei 230000, China.
Science and Technology on Communication Information Security Control Laboratory, Jiaxing 314000, China.
Sensors (Basel). 2020 Mar 18;20(6):1691. doi: 10.3390/s20061691.
In recent years, wireless-based fingerprint positioning has attracted increasing research attention owing to its position-related features and applications in the Internet of Things (IoT). In this paper, by leveraging long-term evolution (LTE) signals, a novel deep-learning-based fingerprint positioning approach is proposed to solve the problem of outdoor positioning. Considering the outstanding performance of deep learning in image classification, LTE signal measurements are converted into location grayscale images to form a fingerprint database. In order to deal with the instability of LTE signals, prevent the gradient dispersion problem, and increase the robustness of the proposed deep neural network (DNN), the following methods are adopted: First, cross-entropy is used as the loss function of the DNN. Second, the learning rate of the proposed DNN is dynamically adjusted. Third, this paper adopted several data enhancement techniques. To find the best positioning fingerprint and method, three types of fingerprint and five positioning models are compared. Finally, by using a deep residual network (Resnet) and transfer learning, a hierarchical structure training method is proposed. The proposed Resnet is used to train with the united fingerprint image database to obtain a positioning model called a coarse localizer. By using the prior knowledge of the pretrained Resnet, feed-forward neural network (FFNN)-based transfer learning is used to train with the united fingerprint database to obtain a better positioning model, called a fine localizer. The experimental results convincingly show that the proposed DNN can automatically learn the location features of LTE signals and achieve satisfactory positioning accuracy in outdoor environments.
近年来，由于具有位置相关特征以及在物联网 (IoT) 中的应用，基于无线的指纹定位技术引起了越来越多的研究关注。在本文中，我们利用长期演进 (LTE) 信号，提出了一种基于深度学习的新型指纹定位方法，以解决户外定位问题。考虑到深度学习在图像分类方面的出色性能，我们将 LTE 信号测量值转换为位置灰度图像，以形成指纹数据库。为了解决 LTE 信号的不稳定性问题，防止梯度弥散问题，并提高所提出的深度神经网络 (DNN) 的鲁棒性，我们采用了以下方法：首先，交叉熵被用作 DNN 的损失函数。其次，动态调整 DNN 的学习率。第三，本文采用了几种数据增强技术。为了找到最佳的定位指纹和方法，我们比较了三种指纹和五种定位模型。最后，通过使用深度残差网络 (Resnet) 和迁移学习，提出了一种分层结构训练方法。使用联合指纹图像数据库对所提出的 Resnet 进行训练，以获得称为粗定位器的定位模型。利用预训练 Resnet 的先验知识，通过基于前馈神经网络 (FFNN) 的迁移学习对联合指纹数据库进行训练，以获得更好的定位模型，称为精定位器。实验结果令人信服地表明，所提出的 DNN 可以自动学习 LTE 信号的位置特征，并在户外环境中实现令人满意的定位精度。
