[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121768-en":3,"doc-seo-121768-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121768,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Robust design of a machine learning-based GNSS NLOS detector with multi-frequency features","Robust detection of GNSS non-line-of-sight (NLOS) signals is essential for safe land-and near-land navigation, where NLOS-affected measurements can cause large, unbounded positioning errors. In urban environments, local threats and complex signal conditions limit purely parametric approaches and motivate machine-learning classification of LOS/NLOS signals. The method introduces pre-normalization of multi-frequency features from open-sky models to improve generalization, and a branched parallel ML pipeline that exploits intermittently available frequencies, yielding higher validation accuracy than prior approaches.","TYPE Original Research PUBLISHED 28 July 2023  \nDOI 10.3389/frobt.2023.1171255  \nOPEN ACCESS  \nEDITED BY  \nLorenzo Carnevale,  \nUniversity of Messina, Italy  \nREVIEWED BY  \nLei Wang,  \nWuhan University, China Yanlei Gu,  \nRitsumeikan University, Japan  \n*CORRESPONDENCE  \nOmar García Crespillo,  \n [Omar.GarciaCrespillo@dlr.de](Omar.GarciaCrespillo@dlr.de)  \nRECEIVED 21 February 2023  \nACCEPTED 27 June 2023  \nPUBLISHED 28 July 2023  \nCITATION  \nGarcía Crespillo O, Ruiz-Sicilia JC, Kliman A and Marais J (2023), Robust design of a machine learning-based  \nGNSS NLOS detector with multi-frequency features.  \nFront. Robot. AI 10:1171255 .  \ndoi: 10.3389/frobt.2023.1171255  \nCOPYRIGHT  \n© 2023 García Crespillo, Ruiz-Sicilia, Kliman and Marais. This is an  \nopen-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nRobust design of a machine learning-based GNSS NLOS detector with multi-frequency features  \nOmar García Crespillo 1*, Juan Carlos Ruiz-Sicilia 1, Ana Kliman 1 and Juliette Marais 2  \n1 Navigation Department, Institute of Communication and Navigation, German Aerospace Center (DLR), Oberpfaffenhofen, Germany, 2 Univ Gustave Eiffel, COSYS-LEOST, Villeneuve d’Ascq, France  \nThe robust detection of GNSS non-line-of-sight (NLOS) signals is of vital importance for land-and close-to-land-based safe navigation applications. The usage of GNSS measurements affected by NLOS can lead to large unbounded positioning errors and loss of safety. Due to the complex signal conditions in urban environments, the use of machine learning or artificial intelligence techniques and algorithms has recently been identified as potential tools to classify GNSS LOS/NLOS signals. The design of machine learning algorithms with GNSS features is an emerging field of research that must, however, be tackled carefully to avoid biased estimation results and to guarantee algorithms that can be generalized for different scenarios, receivers, antennas, and their specific installations and configurations. This work first provides new options to guarantee a proper generalization of trained algorithms by means of a pre-normalization of features with models extracted in open-sky (nominal) scenarios. The second main contribution focuses on designing a branched (or parallel) machine learning process to handle the intermittent presence of GNSS features in certain frequencies. This allows to exploit measurements in all available frequencies as compared to current approaches in the literature based on only the single frequency. The detection by means of logistic regression not only provides a binary LOS/NLOS decision but also an associated probability which can be used in the future as a means to weight-specific measurements. The detection with the proposed branched logistic regression with pre-normalized multi-frequency features has shown better results than the state-of-the-art algorithms, reaching 90% detection accuracy in the validation scenarios evaluated.  \nKEYWORDS  \nglobal navigation satellite system, non-line-of-sight propagation, machine learning, urban environment, local threats  \n1 Introduction  \nGlobal navigation satellite systems (GNSSs) are widely used in transportation applications to localize and navigate vehicles. Compared to aviation, land and close-toland applications suffer from an additional challenge to GNSS positioning: the presence of multiple local threats. These include, among others, multipath, non-line-of-sight (NLOS) signal reception, and interference. Because of these threats, the implementation of GNSS for  \nFrontiers in Robotics and AI 01 [frontiersin.org](frontiersin","cbCaigpcHkYiY6UQ","https://ap.wps.com/l/cbCaigpcHkYiY6UQ","pdf",14921901,1,15,"English","en",105,"# Introduction\n## GNSS positioning challenges in urban environments\n## NLOS definition and impact on pseudorange estimation\n## Limitations of classical NLOS mitigation approaches\n## Motivation for machine learning\n# Key Contributions and Method Overview\n## Pre-normalization for proper generalization across scenarios\n## Branched/parallel processing for multi-frequency availability\n## Logistic regression outputs for LOS/NLOS probability weighting\n# Experimental Evaluation and Results\n## Detection performance in validation scenarios\n## Comparison with state-of-the-art algorithms","[{\"question\":\"Why is robust GNSS NLOS detection important for navigation safety?\",\"answer\":\"NLOS signals can create large unbounded positioning errors, undermining reliability in safety-related navigation. Detecting them prevents unsafe outcomes caused by corrupted pseudorange measurements.\"},{\"question\":\"What problem does the paper address in designing machine-learning GNSS detectors?\",\"answer\":\"It addresses biased or poorly generalized estimation when training and deployment differ across scenarios, receivers, antennas, and configurations. The work focuses on improving generalization and robustness.\"},{\"question\":\"How does the proposed approach use multi-frequency information?\",\"answer\":\"It employs a branched (parallel) machine-learning process that handles intermittent GNSS feature presence across frequencies. This enables exploiting measurements across all available frequencies instead of relying on a single one.\"}]","Robust design of a machine learning-based GNSS NLOS detector with multi-frequency features | PDF",1785806744,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"robust-design-of-a-machine-learning-based-gnss-nlos-detector-with-multi-frequency-features","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/robust-design-of-a-machine-learning-based-gnss-nlos-detector-with-multi-frequency-features/121768/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is robust GNSS NLOS detection important for navigation safety?","Question",{"text":75,"@type":76},"NLOS signals can create large unbounded positioning errors, undermining reliability in safety-related navigation. Detecting them prevents unsafe outcomes caused by corrupted pseudorange measurements.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem does the paper address in designing machine-learning GNSS detectors?",{"text":80,"@type":76},"It addresses biased or poorly generalized estimation when training and deployment differ across scenarios, receivers, antennas, and configurations. The work focuses on improving generalization and robustness.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed approach use multi-frequency information?",{"text":84,"@type":76},"It employs a branched (parallel) machine-learning process that handles intermittent GNSS feature presence across frequencies. 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