[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117303-en":3,"doc-seo-117303-105":29,"detail-sidebar-cat-0-en-105":90},{"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":13,"seo_description":14,"update_tm":27,"read_time":28},117303,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","GNSS localization by machine learning techniques","GNSS localization by machine learning techniques presents an approach to estimate a receiver position using a neural network classifier that exploits GNSS ranging signals such as GPS or Galileo. Instead of relying on Least Squares Positioning (LSP) that minimizes signal residuals, the method targets performance degradation caused by interference, obstructions, and multipath. The network uses satellite positions and pseudoranges to predict a location region via geographical tiling, supported by multi-input architecture, tailored preprocessing, and variability-handling integration.","UNIVERSITY OF PADUA  \nBACHELOR ’S THESIS  \nGNSS localization by machine learning techniques  \nAuthor:  \nFarzam Nikbakhsh Jorshari  \nSupervisor:  \nProf. Stefano Tomasin Prof. Francesco Ardizzon  \nA thesis submitted in fulfilment of the requirements for the degree of Information Engineering in the  \nDepartment of Information Engineering (DEI)  \nAcademic Year: 2023/2024  \nDate of Graduation: 22 November 2024  \n3  \nUNIVERSITY OF PADUA  \nDepartment of Information Engineering (DEI)  \nAbstract  \nInformation Engineering  \nGNSS localization  \nby machine learning techniques  \nby Farzam Nikbakhsh Jorshari  \nThis thesis addresses the problem of accurately determining the location of a receiver using a machine learning-based neural network classifier exploiting rainging signals from a Global Navigation Satellite System (GNSS), such as GPS or Galileo. Traditional GNSS methods, such as Least Squares Positioning (LSP), often focus on minimizing the difference between observed and predicted satellite signals to estimate locations. While these methods work well under ideal conditions, they frequently encounter challenges in environments with significant signal interference, obstructions, or multipath effects, which can degrade their performance.  \nThe proposed solution involves developing a neural network that takes as input the positions of satellites and ranging measurements (pseudoranges) to predict the region, or tile, in which a receiver is located. The concept of‘tiles’ refers to dividing the geographical area into smaller, distinct regions, which simplifies the classification task and allows the model to focus on predicting these predefined zones. Key contributions of this work include the development of a multi-input neural network architecture, tailored preprocessing strategies to ensure data consistency, and integration techniques to handle GNSS data variability effectively.  \n4  \nPerformance analysis focuses on evaluating the accuracy, robustness, and computational efficiency of the model across various scenarios. While the proposed approach shows promise as a scalable alternative to conventional GNSS localization methods, future research is aimed at refining the model architecture and expanding the dataset to improve real-world applicability. This thesis presents a step toward a more adaptable and precise GNSS localization framework, bridging the gap between traditional techniques and modern machine learning advancements.  \n5  \nAcknowledgements  \nI would like to express my sincere gratitude to those who have provided invaluable support and guidance throughout the process of completing this thesis.  \nFirst and foremost, I extend my deepest appreciation to my supervisor, Prof. Stefano Tomasin, for his extensive knowledge, patience, and dedicated mentorship, which have been essential to the completion of this work. His insightful guidance and unwavering commitment to fostering my academic growth have been instrumental in shaping this thesis and deepening my understanding of the field. I consider myself privileged to have had the opportunity to work under his supervision. I am also profoundly grateful to my co-supervisor, Prof. Francesco Ardizzon, for his valuable guidance and support, which provided essential insights that strengthened this thesis. Their combined expertise has greatly enriched this work.  \nI would also like to acknowledge the Department of Information Engineering at the University of Padua for providing the essential resources and facilities necessary to complete this bachelor’s degree.  \nI am deeply thankful to my family—my mother, father, and brother—who, despite the physical distance, have supported me unconditionally. Their encouragement, belief in me, and unwavering support have been a constant source of strength and motivation throughout this journey.  \nFinally, I extend my heartfelt thanks to my friends and classmates, who have been an integral part of my academic experience over the past three years. Together, ","cbCaidy0VGepsH6B","https://ap.wps.com/l/cbCaidy0VGepsH6B","pdf",6190998,1,71,"English","en",105,"# Introduction\n## Problem Statement\n## Existing Solutions and Their Limitations\n## Objective and Contributions of the Thesis\n# Machine Learning Approaches for GNSS Localization\n## Introduction to Machine Learning-Based GNSS Localization\n## Neural Network Architecture for GNSS Localization\n## Regularization Techniques\n## Data Processing and Normalization\n## Loss Function and Optimization\n## Training Strategies","[{\"question\":\"What localization problem does the thesis address?\",\"answer\":\"It addresses accurately determining a receiver’s location using GNSS ranging signals through a machine learning-based neural network classifier.\"},{\"question\":\"How does the proposed method differ from traditional GNSS localization like LSP?\",\"answer\":\"Traditional LSP estimates position by minimizing differences between observed and predicted satellite signals, while the thesis predicts a predefined geographical tile using neural network classification.\"},{\"question\":\"What are the key technical components introduced to improve performance?\",\"answer\":\"The work includes a multi-input neural network architecture, tailored preprocessing for data consistency, and integration techniques to handle GNSS data variability, followed by performance analysis of accuracy, robustness, and 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