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)
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Abstract
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
| Item Type: | Article |
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| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Dead reckoning; Heading estimation; Hierarchical BiLSTM; Indoor localization; Particle swarm optimization |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Centre for Advanced Industrial Technology |
| Depositing User: | Mrs. Nurul Hamira Abd Razak |
| Date Deposited: | 29 Apr 2025 06:37 |
| Last Modified: | 29 Apr 2025 06:37 |
| URI: | http://umpir.ump.edu.my/id/eprint/44411 |
| Download Statistic: | View Download Statistics |
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