Research on geomagnetic indoor high-precision positioning algorithm based on generative model
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...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2023-06-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023104/ |
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| _version_ | 1841540045026099200 |
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| author | Shuai MA Ke PEI Huayan QI Hang LI Wen CAO Hongmei WANG Hailiang XIONG Shiyin LI |
| author_facet | Shuai MA Ke PEI Huayan QI Hang LI Wen CAO Hongmei WANG Hailiang XIONG Shiyin LI |
| author_sort | Shuai MA |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-64665219ae4f440985ea0e83eddcc4bc |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2023-06-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-64665219ae4f440985ea0e83eddcc4bc2025-01-14T06:23:03ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2023-06-014421122259386709Research on geomagnetic indoor high-precision positioning algorithm based on generative modelShuai MAKe PEIHuayan QIHang LIWen CAOHongmei WANGHailiang XIONGShiyin LIAiming 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.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023104/deep learninggeomagnetic positioninggenerative modelgeomagnetic sequence |
| spellingShingle | 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 Tongxin xuebao deep learning geomagnetic positioning generative model geomagnetic sequence |
| title | Research on geomagnetic indoor high-precision positioning algorithm based on generative model |
| title_full | Research on geomagnetic indoor high-precision positioning algorithm based on generative model |
| title_fullStr | Research on geomagnetic indoor high-precision positioning algorithm based on generative model |
| title_full_unstemmed | Research on geomagnetic indoor high-precision positioning algorithm based on generative model |
| title_short | Research on geomagnetic indoor high-precision positioning algorithm based on generative model |
| title_sort | research on geomagnetic indoor high precision positioning algorithm based on generative model |
| topic | deep learning geomagnetic positioning generative model geomagnetic sequence |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023104/ |
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