Research on CNN-BiLSTM-Attention-Based Geomagnetic Positioning Technology

Research on CNN-BiLSTM-Attention-Based Geomagnetic Positioning Technology | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Research on CNN-BiLSTM-Attention-Based Geomagnetic Positioning Technology Qiong Zhang, Haomiao Wang, ShiLin Huang, Zhonghang Ji, Shuang Zheng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7129874/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Geomagnetic positioning is a technology that utilizes the Earth's magnetic field for location determination. It offers advantages such as autonomy, no accumulation of positional errors, absence of signal radiation, and strong concealment. However, its accuracy is influenced by factors such as the adaptability of the geomagnetic matching region and the performance of geomagnetic matching algorithms. To address the problems of poor accuracy and low convergence rate in existing geomagnetic positioning algorithms, a geomagnetic positioning algorithm based on CNN-BiLSTM-Attention is proposed. This algorithm begins by utilizing Convolutional Neural Network (CNN) to process geomagnetic sequences and extract spatial features from the geomagnetic signals. Subsequently, a Bidirectional Long Short-Term Memory (BiLSTM) network is introduced to fully learn the temporal dependency features of the geomagnetic sequence. By employing forward and backward propagation, BiLSTM effectively captures the complex temporal dynamics within geomagnetic signals, enabling a more comprehensive understanding of the variations in geomagnetic sequences. Furthermore, the model incorporates an attention mechanism to dynamically assign importance weights to different time steps, allowing the model to focus on time-step features that contain more significant information. The geomagnetic positioning algorithm based on CNN-BiLSTM-Attention is simulated in three different regions and compared with traditional algorithms as well as the latest algorithms. The experimental results show that the geomagnetic positioning algorithm based on CNN-BiLSTM-Attention achieves a minimum average positioning error of 24.91m. This represents a 32.24% improvement over the CNN-BiLSTM algorithm and a 44.64% improvement over the Gray Wolf Optimization(GWO) algorithm, demonstrating superior geomagnetic positioning accuracy. Geomagnetic Positioning Geomagnetic Reference Map CNN-BiLSTM-Attention positioning error Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 11 May, 2026 Reviews received at journal 02 Mar, 2026 Reviews received at journal 02 Mar, 2026 Reviewers agreed at journal 05 Feb, 2026 Reviewers agreed at journal 03 Feb, 2026 Reviewers invited by journal 03 Feb, 2026 Editor assigned by journal 16 Jul, 2025 Submission checks completed at journal 15 Jul, 2025 First submitted to journal 15 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7129874","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584774049,"identity":"f3478fa3-2b55-4eca-bd00-eb133c0936f5","order_by":0,"name":"Qiong Zhang","email":"","orcid":"","institution":"Changchun University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Qiong","middleName":"","lastName":"Zhang","suffix":""},{"id":584774050,"identity":"9f6307a2-006d-41ef-b676-12546aec4102","order_by":1,"name":"Haomiao Wang","email":"","orcid":"","institution":"Changchun University of Science and 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