Advancing GNSS with Machine Learning: A Systematic Review of Techniques and Applications

Advancing GNSS with Machine Learning: A Systematic Review of Techniques and Applications | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 25 February 2026 V1 Latest version Share on Advancing GNSS with Machine Learning: A Systematic Review of Techniques and Applications Authors : Said Boumaraf 0000-0001-8154-7195 [email protected] , Aymen Dia Eddine Berini , and Mohammed A.B Mahmoud Authors Info & Affiliations https://doi.org/10.22541/au.177204239.94825108/v1 Published Computer Science Review Version of record Peer review timeline 273 views 294 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Global Navigation Satellite Systems (GNSS) play a fundamental role in positioning, navigation, and geosciences, offering reliable solutions under ideal signal conditions. However, challenges such as signal degradation, atmospheric disturbances, and multipath effects often impact accuracy and reliability. Conventional GNSS positioning approaches depend heavily on predefined models that leverage satellite geometry and signal characteristics. While effective under ideal conditions, these methods struggle in complex environments and are often constrained by rigid assumptions about noise and error behavior. Recent developments in machine learning (ML) offer a promising alternative, introducing data-driven adaptability and the ability to learn complex error patterns directly from observational data. Although ML has long been used to advance GNSS applications, a comprehensive assessment of the techniques used, their effectiveness and limitations, and an overview of recent ML-based GNSS applications is still lacking. In this paper, we systematically review the landscape of ML techniques applied to GNSS, ranging from traditional approaches to modern deep architectures and emerging paradigms. Unlike earlier reviews that often assume familiarity with ML, this work offers a brief yet comprehensive overview of the underlying principles, contextualized within GNSS applications. We further examine their usecases in signal classification, error mitigation, and positioning enhancement, while summarizing the datasets employed across the literature. Our findings highlight persistent challenges, including poor generalization across environments, limited annotated data, model interpretability, and deployment constraints on edge devices. We conclude with recommendations for future work, stressing the importance of standardized benchmarks, multi-sensor datasets, and adaptive, resource-efficient models to advance reliable, scalable, and intelligent GNSS systems. Supplementary Material File (advancinggnsswithml_boumaraf_et_al.pdf) Download 1.58 MB Information & Authors Information Version history V1 Version 1 25 February 2026 Peer review timeline Published Computer Science Review Version of Record 1 Aug 2026 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords deep learning geosciences gnss machine learning navigation positioning review Authors Affiliations Said Boumaraf 0000-0001-8154-7195 [email protected] Space Telecommunications Exploitation Center, Algerian Space Agency View all articles by this author Aymen Dia Eddine Berini View all articles by this author Mohammed A.B Mahmoud View all articles by this author Metrics & Citations Metrics Article Usage 273 views 294 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Said Boumaraf, Aymen Dia Eddine Berini, Mohammed A.B Mahmoud. Advancing GNSS with Machine Learning: A Systematic Review of Techniques and Applications. Authorea . 25 February 2026. DOI: https://doi.org/10.22541/au.177204239.94825108/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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