| 摘要: |
| 为降低室内定位指纹数据库构建的复杂度并提高定位精度，提出了一种5G超密集网络下的基于矩阵补全的室内指纹定位算法。在离线数据库构建阶段，算法首先采用K近邻（K-Nearest Neighbor，KNN插值法对部分指纹库进行矩阵补全，构建完整的数据库；其次，采用稀疏自编码器提取指纹库的稀疏特征，对高维接收信号强度指示（Received Signal Strength Indication，RSSI信号进行降维处理。在在线指纹匹配阶段，使用加权 KNN算法估算出待定位点坐标。经过实验仿真分析，算法重构指纹数据库的平均相对误差为0.31%；与传统 KNN指纹匹配算法相比，平均误差降低了24.41%。 |
| 关键词: 5G超密集网络；室内指纹定位 矩阵补全；稀疏自编码 |
| DOI：10.20079/j.issn.1001-893x.240807002 |
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| 基金项目:陕西理工大学研究生创新基金（SLGYCX2461 |
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| Indoor Fingerprint Positioning Based on Matrix Completion in 5G Ultra-dense Network |
| JIANG Haixia,LONG Guangli |
| (School of Physics and Telecommunications Engineering,Shaanxi University of Technology,Hanzhong 723001,China) |
| Abstract: |
| In order to reduce complexity of construction of indoor positioning fingerprint database and improve the positioning accuracy,an indoor fingerprint positioning algorithm based on matrix completion under the 5G ultra-dense network is proposed.In the offline database construction stage,the algorithm first uses the K-nearest Neighbor(KNN interpolation method to complete the matrix of part of the fingerprint database to construct a complete database.Secondly,the sparse auto-encoder is used to extract the sparse features of the fingerprint database,and the high-dimensional received signal strength indication(RSSI signal is reduced.In the online fingerprint matching stage,the weighted KNN algorithm is used to estimate the coordinates of the point to be located.After experimental simulation,the average relative error of the algorithm to reconstruct the fingerprint database is 0.31%.Compared with that of the traditional KNN fingerprint matching algorithm,the average error is reduced by 24.41%. |
| Key words: 5G ultra-dense network indoor fingerprint positioning matrix completion sparse auto-encoding |
