一种无源被动室内区域定位方法的研究

李若南; 李金宝

doi:10.7544/issn1000-1239.2020.20190585

一种无源被动室内区域定位方法的研究

(齐鲁工业大学(山东省科学院)山东省人工智能研究院济南 250014) (黑龙江大学计算机科学技术学院哈尔滨 150080) (黑龙江大学软件学院哈尔滨 150080) (2043331028@qq.com)

基金项目: 国家自然科学基金项目(61370222)；黑龙江省自然科学基金重点项目(ZD2019F003)

详细信息

中图分类号: TP393.17
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出版历程
- 发布日期: 2020-06-30

Research on a Device-free Passive Indoor Regional Localization Method

(Qilu University of Technology (Shandong Academy of Science), Shandong Artificial Intelligence Institute, Jinan 250014) (School of Computer Science and Technology, Heilongjiang University, Harbin 150080) (Software Technology Institute, Heilongjiang University, Harbin 150080)

Funds: This work was supported by the National Natural Science Foundation of China (61370222) and the Key Program of the Natural Science Foundation of Heilongjiang Province of China (ZD2019F003).

摘要

摘要: 室内区域定位在医疗养老、智慧大楼等领域有着广泛的应用.室内区域定位中最突出的问题是无线电信道效应的动态和不可预测性(如多径传播、信道衰落等)对接收信号强度(received signal strength, RSS)的干扰影响.为了降低无线电的干扰，提出了一种新的基于注意力机制的CNN-BiLSTM的室内区域定位模型，该模型通过捕获粗细粒度特征与定位区域的对应关系来减弱RSS序列对信道变化的依赖.首先，利用卷积神经网络(convolutional neural network, CNN)学习捕捉RSS序列的特征来抽取区域中心点的细粒度特征.然后，利用双向长短时记忆(bidirectional long short-term memory, BiLSTM)网络的存储记忆特性，学习当前与过去RSS序列中隐含区域范围的粗粒度特征.最后，利用注意力机制，通过融合粗细粒度特征，建立RSS序列特征与区域位置的映射关系，获取区域位置信息.真实室内环境下区域定位的实验结果表明，与目前定位效果最好的网格区域综合概率定位模型相比，提出的方法在降低计算复杂度的同时提高了区域定位的准确度和对环境的适应能力.
- 室内区域定位 /
- 注意力机制 /
- 接收信号强度 /
- 卷积神经网络 /
- 双向长短时记忆网络
Abstract: Indoor regional localization is widely applied in the fields of medical care, smart buildings and so on. The most prominent problem in indoor area localization is the interference effect of the dynamic and unpredictable nature (such as multipath propagation, channel fading, etc.) of radio channel effect on the received signal strength(RSS). To reduce the radio interference, this paper proposes a new CNN-BiLSTM indoor region localization model of attention-based mechanism, which reduces the dependence of RSS sequence on channel variation by building the relationship between the coarse-grained features and location regions. Above all, the convolutional neural network (CNN) is used to acquire the characteristics of the RSS sequence to extract the fine-grained features of the regional center point. Then, the storage memory characteristics of bidirectional long short-term memory (BiLSTM) is applied to learn the coarse-grained features of the implied region scope in the current and past RSS sequences. Finally, when the attentional mechanism and the fused coarse-grained features are applied, the mapping relationship between RSS sequence features and regional locations is built, and the regional location information is obtained. In real indoor environment,the experimental results of regional location show that compared with the grid region comprehensive probability location model with the best positioning effect,the proposed method improves the accuracy and adaptability of regional location while reducing computational complexity.
- indoor regional positioning /
- attention mechanism /
- received signal strength (RSS) /
- convolutional neural network (CNN) /
- bidirectional long short-term memory (BiLSTM) network

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