[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125132-en":3,"doc-seo-125132-105":30,"detail-sidebar-cat-0-en-105":83},{"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":20,"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},125132,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","LOS/Multipath/NLOS Classifiers using Machine learning and Raytracing - A preliminary study to identify and address the Multipath error","This thesis explores applying machine learning algorithms to mitigate one of the key localization challenges in GNSS: multipath errors. Data is collected with a receiver mounted on an excavator placed in front of a building, used as a multipath source. Ray-tracing simulation in MATLAB supports supervised learning by providing labels for the recorded measurements. A novel feature based on the difference in range acceleration between code and carrier signals is proposed, showing promising separability relative to ray-tracing results. The study evaluates SVM performance (~75% average recall and precision), feature learning via an autoencoder with 1D-CNN layers, and a GRU-based deep neural network to convert multiclass into multi-label multi-output classification.","LOS/Multipath/NLOS Classifiers using Machine learning and Raytracing  \nA preliminary study to identify and address the Mulitpath error Master’s thesis in Complex Adaptive System  \nKARTHIK UPENDRA  \nDEPARTMENT OF SPACE, EARTH and ENVIRONMENT  \nCHALMERS UNIVERSITY OF TECHNOLOGY Gothenburg, Sweden 2024  \n[www.chalmers.se](www.chalmers.se)  \nMaster’s thesis 2024  \nLOS/Multipath/NLOS Classifiers using Machine learning and Raytracing  \nA preliminary study to identify and address the Mulitpath error  \nKARTHIK UPENDRA  \nDepartment of Space Earth and Environment Division of Onsala Space Observatory Research Unit of Space Geodesy and Geodynamics Chalmers University of Technology Gothenburg, Sweden 2024  \nLOS/Multipath/NLOS Classifiers using Machine learning and Raytracing A preliminary study to identify and address the Mulitpath error KARTHIK UPENDRA  \n© KARTHIK UPENDRA, 2024 .  \nSupervisor: Jan Johansson, Department of Space Earth and Environment Examiner: Jan Johansson, Department of Space Earth and Environment  \nMaster’s Thesis 2024  \nJan Johansson, Department of Space Earth and Environment Division of Onsala Space Observatory  \nResearch Unit of Space Geodesy and Geodynamics Chalmers University of Technology  \nSE-412 96 Gothenburg Telephone +46 31 772 1000  \nCover: Discritised Azimuth-Elevation space around the receiver. Diagram not to scale.  \nTypeset in LATEX  \nPrinted by Chalmers Reproservice Gothenburg, Sweden 2024  \nLOS/Multipath/NLOS Classifiers using Machine learning and Raytracing A preliminary study to identify and address the Mulitpath error KARTHIK UPENDRA  \nDepartment of Space Earth and Environment Chalmers University of Technology  \nAbstract  \nThis thesis explores the application of machine learning algorithms to address oneof the challenges in localization in GNSS called the Multipath errors. The approach involves data collection via a receiver mounted on an excavator which is placed in front of the building, which acts as one of the sources for mulitpath error. In order to perform supervised machine learning, wireless communication tool box within Matlab is used for Raytracing simulation to label the data.  \nDrawing inspiration from existing literature, we introduce a novel feature, the ’difference in range acceleration between code and carrier signals,’ which exhibits promising distribution and metrics when analyzed with respect to ray-tracing results.  \nThe support vector machine (SVM) achieves an average class-wise recall and precision of approximately 75% on the recorded measurments. Additionally, we explore the use of an Autoencoder with a 1D-CNN layer to extract new features aimed at enhancing classification performance. By conducting three different simulations with varying data sorting methods, we demonstrate how sorting the data and the machine learning algorithm can influence the learned features which in turn impacts classification performance.  \nLastly, in order to take advantage of the insights gained from different sorting methods, we transform the problem from a Multiclass to Multi-label-Multi-output classification problem, wherein we utilize a deep neural network architecture with GRU units to classify the signals. Although complicated in terms of data restructuring and handling, the network demonstrated robust performance, achieving more than 85% average class-wise recall and precision and exceeding 96% for signals labeled LOS.  \nKeywords: Machine learning, Multipath error, GNSS, Convolutional neural network (CNN), Recurrent neural network (RNN), Gated recurrent unit (GRU), 1D CNN, Autoencoders, Raytracing, Deep neural network (DNN) .  \nAcknowledgements  \nI express my sincere thanks to Professor Jan Johansson for his invaluable guidance and belief in my capabilities throughout this thesis. Special appreciation to my family for their unwavering support. I also acknowledge ChatGPT for its role in structuring and refining my thoughts, contributing to the clarity of this work. Additionally, I appreciate the prompt ass","cbCaigW15jYzibjB","https://ap.wps.com/l/cbCaigW15jYzibjB","pdf",23191912,1,117,"English","en",105,"# Abstract\n## Methodology and Data Collection\n## Feature Engineering and Supervised Learning\n## Model Experiments (SVM, Autoencoder-1D-CNN, DNN-GRU)\n## Problem Transformation: Multiclass to Multi-label Multi-output\n## Keywords","[{\"question\":\"Which models and techniques are evaluated for classifying LOS and NLOS-related signals?\",\"answer\":\"The work evaluates an SVM classifier, an autoencoder with a 1D-CNN layer for feature extraction, and a deep neural network using GRU units, including a conversion from multiclass to multi-label multi-output classification.\"}]","LOS/Multipath/NLOS Classifiers using Machine learning and Raytracing - A preliminary study to identify and address the Multipath error | PDF",1785896835,295,{"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":78,"head_meta":80,"extra_data":82,"updated_unix":28},"losmultipathnlos-classifiers-using-machine-learning-and-raytracing-a-preliminary-study-to-identify-and-address-the-multipath-error","",{"@graph":36,"@context":77},[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/losmultipathnlos-classifiers-using-machine-learning-and-raytracing-a-preliminary-study-to-identify-and-address-the-multipath-error/125132/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"Which models and techniques are evaluated for classifying LOS and NLOS-related signals?","Question",{"text":75,"@type":76},"The work evaluates an SVM classifier, an autoencoder with a 1D-CNN layer for feature extraction, and a deep neural network using GRU units, including a conversion from multiclass to multi-label multi-output classification.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,127],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":98,"slug":130},19,"General","general"]