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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/5/1304 |
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| author | Suqing Yan Baihui Luo Xiyan Sun Jianming Xiao Yuanfa Ji Kamarul Hawari bin Ghazali |
| author_facet | Suqing Yan Baihui Luo Xiyan Sun Jianming Xiao Yuanfa Ji Kamarul Hawari bin Ghazali |
| author_sort | Suqing Yan |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-a951e9ab7f764aebaab4c57066d5a4a9 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-a951e9ab7f764aebaab4c57066d5a4a92025-08-20T02:59:08ZengMDPI AGSensors1424-82202025-02-01255130410.3390/s25051304Accurate Localization Method Combining Optimized Hybrid Neural Networks for Geomagnetic Localization with Multi-Feature Dead ReckoningSuqing Yan0Baihui Luo1Xiyan Sun2Jianming Xiao3Yuanfa Ji4Kamarul Hawari bin Ghazali5Guangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Technology, Guilin 541004, ChinaSchool of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, ChinaNational & Local Joint Engineering Research Center of Satellite Navigation Localization and Location Service, Guilin 541004, ChinaSchool of Science and Technology, Guilin University, Guilin 541006, ChinaNational & Local Joint Engineering Research Center of Satellite Navigation Localization and Location Service, Guilin 541004, ChinaCenter for Advanced Industrial Technology, University Malaysia Pahang Al Sultan Abdullah, Pekan 26600, MalaysiaLocation-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.https://www.mdpi.com/1424-8220/25/5/1304indoor localizationparticle swarm optimizationhierarchical BiLSTMheading estimationdead reckoning |
| spellingShingle | Suqing Yan Baihui Luo Xiyan Sun Jianming Xiao Yuanfa Ji Kamarul Hawari bin Ghazali Accurate Localization Method Combining Optimized Hybrid Neural Networks for Geomagnetic Localization with Multi-Feature Dead Reckoning Sensors indoor localization particle swarm optimization hierarchical BiLSTM heading estimation 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 | indoor localization particle swarm optimization hierarchical BiLSTM heading estimation dead reckoning |
| url | https://www.mdpi.com/1424-8220/25/5/1304 |
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