一种室内伪卫星混合指纹定位方法

doi:10.19665/j.issn1001-2400.20221102

Abstract

Abstract:

At present,the interaction mechanism between the complex indoor environment and pseudolite signals has not been fundamentally resolved,and the stability,continuity,and accuracy of indoor positioning are still technical bottlenecks.Existing fingerprint positioning methods face the limitation that the collection workload is proportional to the positioning accuracy and positioning range,and have the disadvantage that the positioning cannot be completed without actual collection.In order to solve the above shortcomings of the existing methods,by combining the advantages of actual measurement,mathematical simulation and the artificial neural network,an indoor pseudolite hybrid fingerprint location method based on actual fingerprints,simulation fingerprints and the artificial neural network is proposed.First,the actual environment and signal transceiver are modeled.Second,both the simulated fingerprints generated by ray tracing simulation after conversion and the measured fingerprints are added to the input of the neural network,which expands the sample characteristics of the input data set of the original single measured fingerprints.Finally,the artificial neural network positioning model is jointly trained by the mixed fingerprints and then used for online positioning.By taking an airport environment as an example,it is proved that the hybrid method can improve the positioning accuracy of the sparsely collected fingerprint region,and that the root mean square error is 0.485 0 m,which is 54.7% lower than that of the traditional fingerprint positioning method.Preliminary positioning can also be completed in areas where no fingerprints are collected,and the root mean square positioning error is 1.123 7 m,which breaks through the limitations of traditional fingerprint location methods.

Key words: fingerprint positioning, ANN, pseudolite, ray tracing, indoor positioning

CLC Number:

TN967.1

LI Yaning,LI Hongsheng,YU Baoguo. Indoor pseudolite hybrid fingerprint positioning method[J].Journal of Xidian University, 2023, 50(5): 21-31.

Figures/Tables 11

References 16

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参数	值
房间模型尺寸/m³	100×20×4.5
承重柱模型尺寸/m³	0.5×0.5×4.5
墙壁材质/介电常数/电导率	混凝土/5/0.001 5
地面材质/介电常数/电导率	瓷砖大理石/6/10^-8
信号频率/MHz	1 575.42
天线类型	右旋圆极化全向天线
仿真测试接收区域尺寸/m²	5×5
仿真测试接收点间隔/m	1
发射功率/dBm	-70
最大反射/透射/绕射次数	3
射线间隔/(°)	0.25

算法	K最邻近法	支持向量法	文献[3]方法	文献[9]方法	文中方法
RMS误差/m	1.182 1	1.069 6	0.771 4	0.925 3	0.485 0
95%误差/m	2.272 7	1.842 4	1.619 4	1.472 7	1.026 0
平均绝对误差/m	0.351 5	0.333 3	0.260 7	0.305 2	0.145 5

Indoor pseudolite hybrid fingerprint positioning method

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