School of Electronic Countermeasures, National University of Defense Technology, Hefei 230000, China.
Sensors (Basel). 2019 Nov 26;19(23):5180. doi: 10.3390/s19235180.
Fingerprint-based positioning techniques are a hot research topic because of their satisfactory accuracy in complex environments. In this study, we adopted the deep-learning-based long-time-evolution (LTE) signal fingerprint positioning method for outdoor environment positioning. Inspired by state-of-the-art image classification methods, a novel hybrid location gray-scale image utilizing LTE signal fingerprints is proposed in this paper. In order to deal with signal fluctuations, several data enhancement methods are adopted. A hierarchical architecture is put forward during the deep neural network (DNN) training. First, the proposed positioning technique is pre-trained by a modified Deep Residual Network (Resnet) coarse localizer which is capable of learning reliable features from a set of unstable LTE signals. Then, to alleviate the tremendous collection workload, as well as further improve the positioning accuracy, by using a multilayer perceptron (MLP), a transfer learning-based fine localizer is introduced for fine-tuning the coarse localizer. The experimental data was collected from realistic scenes to meet the requirement of actual environments. The experimental results show that the proposed system leads to a considerable positioning accuracy in a variety of outdoor environments.
基于指纹的定位技术是一个热门的研究课题，因为它们在复杂环境中的定位精度令人满意。在这项研究中，我们采用了基于深度学习的长时间演进 (LTE) 信号指纹定位方法进行户外环境定位。受最新图像分类方法的启发，本文提出了一种利用 LTE 信号指纹的新型混合位置灰度图像。为了处理信号波动，采用了几种数据增强方法。在深度神经网络 (DNN) 训练过程中提出了一种分层架构。首先，通过修改的深度残差网络 (Resnet) 粗定位器对所提出的定位技术进行预训练，该粗定位器能够从一组不稳定的 LTE 信号中学习到可靠的特征。然后，为了减轻巨大的采集工作量，并进一步提高定位精度，通过使用多层感知机 (MLP)，引入基于迁移学习的精细定位器对粗定位器进行微调。实验数据是从真实场景中收集的，以满足实际环境的要求。实验结果表明，该系统在各种户外环境中都能实现相当高的定位精度。
