| 薛 敏,孙 炜,余洪山,张 星.基于 WiFi 指纹的层级学习室内定位模型[J].电子测量与仪器学报,2021,35(4):118-126 |
| 基于 WiFi 指纹的层级学习室内定位模型 |
| Hierarchical deep learning model to locate the mobiledevice via WiFi fingerprints |
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| DOI： |
| 中文关键词: 位置管理 WiFi 指纹 室内定位 层级学习模型 |
| 英文关键词:location management WiFi fingerprint indoor localization hierarchical neural network |
| 基金项目:国家自然科学基金（U1813205）、汽车车身先进设计制造国家重点实验室（71765003）、电子制造业智能机器人技术湖南省重点实验室（2017TP1011）项目资助 |
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| Author | Institution |
| Xue Min | 1. College of Electrical and Information Engineering,Hunan University,2. National Engineering Laboratory for Robot Vision Perception and Control Technologies,3. Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing |
| Sun Wei | 1. College of Electrical and Information Engineering,Hunan University,2. National Engineering Laboratory for Robot Vision Perception and Control Technologies,3. Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing |
| Yu Hongshan | 1. College of Electrical and Information Engineering,Hunan University,2. National Engineering Laboratory for Robot Vision Perception and Control Technologies,3. Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing |
| Zhang Xing | 1. College of Electrical and Information Engineering,Hunan University,3. Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing |
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| 中文摘要: |
| 随着物联网和信息技术的飞速发展,基于移动位置的服务近年来日益受到关注,同时也促进了室内定位技术的发展。
基于 WiFi 指纹的室内定位技术以其部署广泛、成本低廉等优点受到了学术界的广泛研究。 针对移动设备在室内环境中的定位
问题,提出了一种层级学习室内定位系统(hierarchical deep learning indoor localization framework, HDLIL)。 为获取和学习可靠的
指纹特征,采用基于变分自编码(variational autoencoder, VAE)的特征提取模块来表征训练数据的潜在表示。 通过构建多层神
经网络来分析输入特征与位置输出之间的关系,并在输出层连接 Softmax 分类器,预测移动设备的位置。 在定位阶段,移动设备
接收测试数据并发送定位请求,然后通过加载 HDLIL 估计该测试指纹的位置。 最后通过实验对 HDLIL 的定位性能进行了评
估,讨论了不同定位因素对结果的影响,验证了该定位算法的精度及鲁棒性。 |
| 英文摘要: |
| With rapid development of internet of things ( IOT) and information technology, mobile location-based service ( LBS) is
gaining more and more research focus in recent years. It also stimulates the development of indoor localization technology. Owing to the
advantage of its pervasive deployment, WiFi fingerprint-based indoor localization has drawn much attention to academia. However, the
fluctuation of WiFi signals and other interference always influence the localization performance. In this paper, a hierarchical deep
learning indoor localization framework (HDLIL) is proposed to solve the problem of mobile device localization in indoor environments
and predict the specific position. To capture and learn the reliable fingerprint features, a feature extraction module based on variational
autoencoder (VAE) is introduced to characterize the latent representation of the training data. Also, to train the localization model, we
feed the reconstructed training fingerprints as well as corresponding labels to a 3-layer deep neural network, of which the output of
current layer is set as the input for the next layer, followed by a location output module based on the concatenated softmax classification.
In the localization phase, the localization fingerprints ( testing fingerprints) is set as the input, the HDLIL model is invoked and the
output of final layer is the predicted location of the mobile device. In addition, to evaluate the localization performance, we conducted
the experiment in a real indoor scene, and several influence factors are discussed. The result indicates that the proposed HDLIL model
can attain a superior localization performance. |
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