基于生成模型的地磁室内高精度定位算法研究

doi:10.11959/j.issn.1000-436x.2023104

Abstract

Abstract:

Aiming at the current bottleneck of constructing a fine geomagnetic fingerprint library that required a lot of labor costs, two generative models called the conditional variational autoencoder and the conditional confrontational generative network were proposed, which could collect a small number of data samples for a given location, and generate pseudo-label fingerprints.At the same time, in order to solve the problem of low positioning accuracy of single-point geomagnetic fingerprints, a geomagnetic sequence positioning algorithm based on attention mechanism of convolutional neural network-gated recurrent unit was designed, which could effectively use the spatial and temporal characteristics of fingerprints to achieve precise positioning.In addition, a real-time, portable mobile terminal data collection and positioning system was also designed and built.The actual test shows that the proposed model can effectively construct the available geomagnetic fingerprint database, and the average error of the proposed algorithm can reach 0.16 m.

Key words: deep learning, geomagnetic positioning, generative model, geomagnetic sequence

CLC Number:

TN92

Shuai MA, Ke PEI, Huayan QI, Hang LI, Wen CAO, Hongmei WANG, Hailiang XIONG, Shiyin LI. Research on geomagnetic indoor high-precision positioning algorithm based on generative model[J]. Journal on Communications, 2023, 44(6): 211-222.

Figures/Tables 23

References 24

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参数	数值
移动设备尺寸	10 cm×8 cm×8 cm
处理器	树莓派4B
九轴传感器	MPU9250
液晶显示屏	3.3 V/5 V

数据库	样本数量/个	生成模型	平均定位误差/m
GMFD-1	40 000	CGAN	0.25
GMFD-2	40 000	CVAE	0.26
MFD	10 000	—	0.32

参考点间隔/m	参考点数量/个	训练集样本个数/个
0.2	400	40 000
0.4	100	10 000
0.6	49	4 900
0.8	36	3 600
1.0	25	2 500
2.0	9	900

算法	最小误差/m	最大误差/m	方差/m²	均方根误差/m	定位时间/ms
MLP	0.01	1.17	0.032	0.25	0.06
RNN	0.01	1.16	0.017	0.21	0.84
本文算法	0.01	1.13	0.013	0.16	3.54

算法	深度学习算法	定位时间/ms	准确度/m	处理方式
文献[24]	LSTM	0.89	0.21	离线处理
文献[17]	LSTM+Attention	1.39	0.20	离线处理
本文算法	CNN+GRU+Attention	3.54	0.16	在线处理

Research on geomagnetic indoor high-precision positioning algorithm based on generative model

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