Multi-Output Regression Indoor Localization Algorithm Based on Hybrid Grey Wolf Particle Swarm Optimization
In the evolving landscape of device-free localization techniques, Wi-Fi channel state information (CSI) emerges as a pivotal tool for environmental sensing. This study introduces a novel fingerprint localization algorithm. It employs an improved Hybrid Grey Wolf Particle Swarm Optimization (IPSOGWO)...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/22/12167 |
| _version_ | 1797460293661491200 |
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| author | Shicheng Xie Xuexiang Yu Zhongchen Guo Mingfei Zhu Yuchen Han |
| author_facet | Shicheng Xie Xuexiang Yu Zhongchen Guo Mingfei Zhu Yuchen Han |
| author_sort | Shicheng Xie |
| collection | DOAJ |
| description | In the evolving landscape of device-free localization techniques, Wi-Fi channel state information (CSI) emerges as a pivotal tool for environmental sensing. This study introduces a novel fingerprint localization algorithm. It employs an improved Hybrid Grey Wolf Particle Swarm Optimization (IPSOGWO) in combination with Multi-Output Support Vector Regression (MSVR) to enhance indoor positioning accuracy. To counteract the limitations of standard DBSCAN and PCA in noise reduction and feature extraction from complex nonlinear data, we propose an adaptive denoising algorithm based on spatial clustering (A-DBSCAN) and an autoencoder to efficiently denoise and extract features from CSI amplitude to improve the localization accuracy. Additionally, we introduce a new position update strategy, bolstering the optimization efficiency of the PSOGWO algorithm. This refined approach is instrumental in determining the globally optimal hyperparameters in MSVR, leading to enhanced model prediction accuracy. Two indoor scenario experiments were conducted to evaluate our method, yielding average localization errors of 0.59 m and 1.12 m, marking an improvement in localization performance compared to existing methods. |
| first_indexed | 2024-03-09T17:03:08Z |
| format | Article |
| id | doaj.art-05c3e82e8eb04525a53bdebed495745a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T17:03:08Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-05c3e82e8eb04525a53bdebed495745a2023-11-24T14:26:27ZengMDPI AGApplied Sciences2076-34172023-11-0113221216710.3390/app132212167Multi-Output Regression Indoor Localization Algorithm Based on Hybrid Grey Wolf Particle Swarm OptimizationShicheng Xie0Xuexiang Yu1Zhongchen Guo2Mingfei Zhu3Yuchen Han4School of Earth and Environment, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Earth and Environment, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Environment and Surveying Engineering, Suzhou University, Suzhou 234000, ChinaSchool of Earth and Environment, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Earth and Environment, Anhui University of Science and Technology, Huainan 232001, ChinaIn the evolving landscape of device-free localization techniques, Wi-Fi channel state information (CSI) emerges as a pivotal tool for environmental sensing. This study introduces a novel fingerprint localization algorithm. It employs an improved Hybrid Grey Wolf Particle Swarm Optimization (IPSOGWO) in combination with Multi-Output Support Vector Regression (MSVR) to enhance indoor positioning accuracy. To counteract the limitations of standard DBSCAN and PCA in noise reduction and feature extraction from complex nonlinear data, we propose an adaptive denoising algorithm based on spatial clustering (A-DBSCAN) and an autoencoder to efficiently denoise and extract features from CSI amplitude to improve the localization accuracy. Additionally, we introduce a new position update strategy, bolstering the optimization efficiency of the PSOGWO algorithm. This refined approach is instrumental in determining the globally optimal hyperparameters in MSVR, leading to enhanced model prediction accuracy. Two indoor scenario experiments were conducted to evaluate our method, yielding average localization errors of 0.59 m and 1.12 m, marking an improvement in localization performance compared to existing methods.https://www.mdpi.com/2076-3417/13/22/12167indoor positioningchannel state informationhybrid optimization algorithmMSVR |
| spellingShingle | Shicheng Xie Xuexiang Yu Zhongchen Guo Mingfei Zhu Yuchen Han Multi-Output Regression Indoor Localization Algorithm Based on Hybrid Grey Wolf Particle Swarm Optimization Applied Sciences indoor positioning channel state information hybrid optimization algorithm MSVR |
| title | Multi-Output Regression Indoor Localization Algorithm Based on Hybrid Grey Wolf Particle Swarm Optimization |
| title_full | Multi-Output Regression Indoor Localization Algorithm Based on Hybrid Grey Wolf Particle Swarm Optimization |
| title_fullStr | Multi-Output Regression Indoor Localization Algorithm Based on Hybrid Grey Wolf Particle Swarm Optimization |
| title_full_unstemmed | Multi-Output Regression Indoor Localization Algorithm Based on Hybrid Grey Wolf Particle Swarm Optimization |
| title_short | Multi-Output Regression Indoor Localization Algorithm Based on Hybrid Grey Wolf Particle Swarm Optimization |
| title_sort | multi output regression indoor localization algorithm based on hybrid grey wolf particle swarm optimization |
| topic | indoor positioning channel state information hybrid optimization algorithm MSVR |
| url | https://www.mdpi.com/2076-3417/13/22/12167 |
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