Accurate localization method combining optimized hybrid neural networks for geomagnetic localization with multi-feature dead reckoning
Location-based services provide significant economic and social benefits. The ubiquity, low cost, and accessibility of geomagnetism are highly advantageous for localization. However, the existing geomagnetic localization methods suffer from location ambiguity. To address these issues, we propose a f...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI
2025
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| Online Access: | http://umpir.ump.edu.my/id/eprint/44411/ http://umpir.ump.edu.my/id/eprint/44411/1/Accurate%20localization%20method%20combining%20optimized%20hybrid%20neural%20networks.pdf |
| _version_ | 1848827097570607104 |
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| author | Yan, Suqing Luo, Baihui Sun, Xiyan Xiao, Jianming Ji, Yuanfa Kamarul Hawari, Ghazali |
| author_facet | Yan, Suqing Luo, Baihui Sun, Xiyan Xiao, Jianming Ji, Yuanfa Kamarul Hawari, Ghazali |
| author_sort | Yan, Suqing |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Location-based services provide significant economic and social benefits. The ubiquity, low cost, and accessibility of geomagnetism are highly advantageous for localization. However, the existing geomagnetic localization methods suffer from location ambiguity. To address these issues, we propose a fusion localization algorithm based on particle swarm optimization. First, we construct a five-dimensional hybrid LSTM (5DHLSTM) neural network model, and the 5DHLSTM network structure parameters are optimized via particle swarm optimization (PSO) to achieve geomagnetic localization. The eight-dimensional BiLSTM (8DBiLSTM) algorithm is subsequently proposed for heading estimation in dead reckoning, which effectively improves the heading accuracy. Finally, fusion localization is achieved by combining geomagnetic localization with an improved pedestrian dead reckoning (IPDR) based on an extended Kalman filter (EKF). To validate the localization performance of the proposed PSO-5DHLSTM-IPDR method, several extended experiments using Xiaomi 10 and Hi Nova 9 are conducted in two different scenarios. The experimental results demonstrate that the proposed method improves localization accuracy and has good robustness and flexibility |
| first_indexed | 2025-11-15T03:55:18Z |
| format | Article |
| id | ump-44411 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:55:18Z |
| publishDate | 2025 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-444112025-04-29T06:37:41Z http://umpir.ump.edu.my/id/eprint/44411/ Accurate localization method combining optimized hybrid neural networks for geomagnetic localization with multi-feature dead reckoning Yan, Suqing Luo, Baihui Sun, Xiyan Xiao, Jianming Ji, Yuanfa Kamarul Hawari, Ghazali QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Location-based services provide significant economic and social benefits. The ubiquity, low cost, and accessibility of geomagnetism are highly advantageous for localization. However, the existing geomagnetic localization methods suffer from location ambiguity. To address these issues, we propose a fusion localization algorithm based on particle swarm optimization. First, we construct a five-dimensional hybrid LSTM (5DHLSTM) neural network model, and the 5DHLSTM network structure parameters are optimized via particle swarm optimization (PSO) to achieve geomagnetic localization. The eight-dimensional BiLSTM (8DBiLSTM) algorithm is subsequently proposed for heading estimation in dead reckoning, which effectively improves the heading accuracy. Finally, fusion localization is achieved by combining geomagnetic localization with an improved pedestrian dead reckoning (IPDR) based on an extended Kalman filter (EKF). To validate the localization performance of the proposed PSO-5DHLSTM-IPDR method, several extended experiments using Xiaomi 10 and Hi Nova 9 are conducted in two different scenarios. The experimental results demonstrate that the proposed method improves localization accuracy and has good robustness and flexibility MDPI 2025 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/44411/1/Accurate%20localization%20method%20combining%20optimized%20hybrid%20neural%20networks.pdf Yan, Suqing and Luo, Baihui and Sun, Xiyan and Xiao, Jianming and Ji, Yuanfa and Kamarul Hawari, Ghazali (2025) Accurate localization method combining optimized hybrid neural networks for geomagnetic localization with multi-feature dead reckoning. Sensors, 25 (5). pp. 1-24. ISSN 1424-8220. (Published) https://doi.org/10.3390/s25051304 https://doi.org/10.3390/s25051304 |
| spellingShingle | QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Yan, Suqing Luo, Baihui Sun, Xiyan Xiao, Jianming Ji, Yuanfa Kamarul Hawari, Ghazali Accurate localization method combining optimized hybrid neural networks for geomagnetic localization with multi-feature dead reckoning |
| title | Accurate localization method combining optimized hybrid neural networks for geomagnetic localization with multi-feature dead reckoning |
| title_full | Accurate localization method combining optimized hybrid neural networks for geomagnetic localization with multi-feature dead reckoning |
| title_fullStr | Accurate localization method combining optimized hybrid neural networks for geomagnetic localization with multi-feature dead reckoning |
| title_full_unstemmed | Accurate localization method combining optimized hybrid neural networks for geomagnetic localization with multi-feature dead reckoning |
| title_short | Accurate localization method combining optimized hybrid neural networks for geomagnetic localization with multi-feature dead reckoning |
| title_sort | accurate localization method combining optimized hybrid neural networks for geomagnetic localization with multi-feature dead reckoning |
| topic | QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/44411/ http://umpir.ump.edu.my/id/eprint/44411/ http://umpir.ump.edu.my/id/eprint/44411/ http://umpir.ump.edu.my/id/eprint/44411/1/Accurate%20localization%20method%20combining%20optimized%20hybrid%20neural%20networks.pdf |