| 摘要: |
| 针对室内环境中多径效应影响定位精度的问题，提出了一种基于卷积神经网络（CNN）的室内定位（PI-CNN）算法.以多重信号分类（MUSIC）算法处理后的信道状态信息（CSI）作为特征图像，基于室内环境中不同位置点具有独特多径信息的特点，利用各收发天线间所形成的子信道信息，获得具有更高时间分辨率的多径到达时间，将获取的伪谱信息组成伪谱图像，生成指纹库，再利用CNN进行训练和分类处理.仿真实验证明，在室内环境存在轻微扰动的情况下，该算法具有较好的抗干扰能力. |
| 关键词: 深度卷积神经网络(CNN) 多重信号分类(MUSIC)算法 信道状态信息(CSI) 指纹定位 |
| DOI：10.3969/J.ISSN.1000-5137.2021.01.013 |
| 分类号:TN929.5 |
| 基金项目: |
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| CNN-based super-resolution channel impulse response indoor fingerprint location algorithm |
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LUO Kaiwen, YU Hui
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School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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| Abstract: |
| Aiming at the problem that the multipath effect in the indoor environment affected the positioning accuracy, based on a deep convolutional neural network(CNN), pseudo spectral image-CNN(PI-CNN)algorithm was proposed in this paper. Using channel state information processed by multiple signal classification(MUSIC)algorithm as a feature image, based on the unique multipath information of different locations in the indoor environment,the sub-channel information formed between the transceiver antennas was utilized to process the channel state information(CSI)to obtain the multipath arrival time with higher time resolution. The pseudo-spectral information of all antennas at the same sampling point was constructed into pseudo-spectral images to generate a fingerprint library which were used to train the CNN. The simulation experiments showed that the PI-CNN algorithm performed well when dealing with slight disturbance in the indoor environment. |
| Key words: deep convolutional neural network(CNN) multiple signal classification(MUSIC)algorithm channel state information(CSI) fingerprint location |
