High-precision indoor wireless positioning method based on generative adversarial network
Because wireless signals are susceptible to interference during the propagation process, the application of traditional indoor positioning methods in real life is limited.Because location-based fingerprint positioning technology has the advantage of strong universality, it has become a current resea...
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| Format: | Article |
| Language: | zho |
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China InfoCom Media Group
2021-06-01
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| Series: | 物联网学报 |
| Subjects: | |
| Online Access: | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2021.00208/ |
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| _version_ | 1841531114879975424 |
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| author | Fuzhan WANG Xiaorong ZHU Meijuan CHEN Hongbo ZHU |
| author_facet | Fuzhan WANG Xiaorong ZHU Meijuan CHEN Hongbo ZHU |
| author_sort | Fuzhan WANG |
| collection | DOAJ |
| description | Because wireless signals are susceptible to interference during the propagation process, the application of traditional indoor positioning methods in real life is limited.Because location-based fingerprint positioning technology has the advantage of strong universality, it has become a current research hotspot.The number of fingerprint data is an important factor affecting the accuracy of fingerprint positioning, but the cost of collecting a large amount of fingerprint data is large.Therefore, how to use a small amount of fingerprint data to achieve higher positioning accuracy is a difficult point of fingerprint positioning technology.Aiming at this problem, a high-precision indoor wireless positioning method based on generative adversarial network was proposed.Firstly, fingerprint data was collected densely at equal intervals indoors, and the initial fingerprint data set was constructed, the part of the fingerprint data was selected in the initial fingerprint data set, and the generative adversarial network was used to obtain a large amount of fingerprint data from part of the fingerprint data.Then, based on these generated data, a KNN (k-nearest neighbor) model and a random forest model were used for location prediction.Experimental results show that this method can achieve high wireless positioning accuracy based on a small amount of fingerprint data, and the positioning accuracy can reach 15.4 cm. |
| format | Article |
| id | doaj-art-d5c125f3fddc464a86abde0ab2f94000 |
| institution | Kabale University |
| issn | 2096-3750 |
| language | zho |
| publishDate | 2021-06-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 物联网学报 |
| spelling | doaj-art-d5c125f3fddc464a86abde0ab2f940002025-01-15T02:53:41ZzhoChina InfoCom Media Group物联网学报2096-37502021-06-01510711559650058High-precision indoor wireless positioning method based on generative adversarial networkFuzhan WANGXiaorong ZHUMeijuan CHENHongbo ZHUBecause wireless signals are susceptible to interference during the propagation process, the application of traditional indoor positioning methods in real life is limited.Because location-based fingerprint positioning technology has the advantage of strong universality, it has become a current research hotspot.The number of fingerprint data is an important factor affecting the accuracy of fingerprint positioning, but the cost of collecting a large amount of fingerprint data is large.Therefore, how to use a small amount of fingerprint data to achieve higher positioning accuracy is a difficult point of fingerprint positioning technology.Aiming at this problem, a high-precision indoor wireless positioning method based on generative adversarial network was proposed.Firstly, fingerprint data was collected densely at equal intervals indoors, and the initial fingerprint data set was constructed, the part of the fingerprint data was selected in the initial fingerprint data set, and the generative adversarial network was used to obtain a large amount of fingerprint data from part of the fingerprint data.Then, based on these generated data, a KNN (k-nearest neighbor) model and a random forest model were used for location prediction.Experimental results show that this method can achieve high wireless positioning accuracy based on a small amount of fingerprint data, and the positioning accuracy can reach 15.4 cm.http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2021.00208/fingerprint localizationgenerative adversarial networkindoorKNNrandom forest |
| spellingShingle | Fuzhan WANG Xiaorong ZHU Meijuan CHEN Hongbo ZHU High-precision indoor wireless positioning method based on generative adversarial network 物联网学报 fingerprint localization generative adversarial network indoor KNN random forest |
| title | High-precision indoor wireless positioning method based on generative adversarial network |
| title_full | High-precision indoor wireless positioning method based on generative adversarial network |
| title_fullStr | High-precision indoor wireless positioning method based on generative adversarial network |
| title_full_unstemmed | High-precision indoor wireless positioning method based on generative adversarial network |
| title_short | High-precision indoor wireless positioning method based on generative adversarial network |
| title_sort | high precision indoor wireless positioning method based on generative adversarial network |
| topic | fingerprint localization generative adversarial network indoor KNN random forest |
| url | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2021.00208/ |
| work_keys_str_mv | AT fuzhanwang highprecisionindoorwirelesspositioningmethodbasedongenerativeadversarialnetwork AT xiaorongzhu highprecisionindoorwirelesspositioningmethodbasedongenerativeadversarialnetwork AT meijuanchen highprecisionindoorwirelesspositioningmethodbasedongenerativeadversarialnetwork AT hongbozhu highprecisionindoorwirelesspositioningmethodbasedongenerativeadversarialnetwork |