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一种融合5G CSI和地磁的集成学习定位方法

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针对深度学习算法在多传感器融合定位中容易出现的局部收敛、异质融合性能不佳等问题,本文提出了一种集成双向长短期记忆网络(BiLSTM)和注意力机制的多输入卷积神经网络(CNN)的室内定位算法.该算法首先对5G信道状态信息(CSI)和地磁数据分别进行预处理;然后各自基于独立的分支网络进行离线训练,同时提取指纹数据的空间特征和时序特征,追加注意力机制层;最后在全连接层实现了异质传感器数据的融合定位.在会议室和教学楼大厅的试验结果表明,平均定位误差分别为0.95和1.84 m,相比误差反向传播网络(BPNN)分别提高了 48.9％和42.7％,定位精度和系统稳定性均大幅提升.
An integrated learning localization method fusing 5G CSI and geomagnetic data
A multi-input convolutional neural network(CNN)incorporating bidirectional long short-term memory neural network(BiLSTM)and attention mechanism is presented to address the issues of local convergence and poor heterogeneous fusion performance of deep learning algorithms in multi-sensor fusion positioning.Firstly,5G channel state information(CSI)and geomagnetic data arepreprocessed separately.Then each of them is trained offline based on an independent branch network,and the spatial and temporal features of the fingerprint data are extracted at the sametime to append the attention mechanismlayer.Finally,the fusion of heterogeneoussensor data for localization is achieved at the fully connected layer.The experimental results in the conference room and the teaching building hall show that the average positioning error is 0.95 and 1.84 m respectively,which is 48.9％and 42.7％higher than that of the error backpropagation network(BPNN),and that positioning accuracy and system stability are both greatly improved.

indoor positioningconvolutional neural network(CNN)bidirectional long short-term memory neural network(BiLSTM)attention mechanismchannel state information(CSI)

程振豪、赵冬青、郭文卓、赖路广、李林阳

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信息工程大学地理空间信息学院,河南郑州 450001

室内定位 卷积神经网络(CNN) 双向长短期记忆神经网络(BiLSTM) 注意力机制 信道状态信息(CSI)

国家自然科学基金国家自然科学基金

421040341774037

2024

10.13474/j.cnki.11-2246.2024.0703

测绘通报
测绘出版社

测绘通报

CSTPCD北大核心
影响因子：1.027
ISSN：0494-0911
年,卷(期)：2024.(7)
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