[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127147-en":3,"doc-seo-127147-105":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},127147,3985741905716,"Rowan","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","A Survey of Machine Learning Techniques for Improving Global Navigation Satellite Systems - A comprehensive overview","Global Navigation Satellite Systems (GNSS)-based positioning underpins navigation, transportation, logistics, mapping, and emergency services. Traditional model-based methods rely on satellite geometry and signal properties, yet they often struggle in challenging environments and lack adaptability to uncertain noise conditions. This survey consolidates recent advances in machine learning—supervised, unsupervised, deep learning, and hybrid methods—for GNSS positioning. It covers applications such as signal analysis, anomaly detection, multi-sensor integration, prediction, and accuracy enhancement, while outlining strengths, limitations, and key challenges.","arXiv :2406 . 16873v1 [ ee ss . SP] 29 Mar 2024  \nA Survey of Machine Learning Techniques for Improving Global Navigation Satellite Systems  \nAdyasha Mohanty and Grace Gao*  \nAeronautics and Astronautics, Stanford University, 496 Lomita Mall, Palo Alto, 94305, CA, USA.  \n*Corresponding author. E-mail: [gracegao@stanford.edu](gracegao@stanford.edu).  \nAbstract  \nGlobal Navigation Satellite Systems (GNSS)-based positioning plays a crucial role in various applications, including navigation, transportation, logistics, mapping, and emergency services. Traditional GNSS positioning methods are model-based and they utilize satellite geometry and the known properties of satellite signals.  \nHowever, model-based methods have limitations in challenging environments and often lack adaptability to uncertain noise models. This paper highlights recent advances in Machine Learning (ML) and its potential to address these limitations. It covers a broad range of ML methods, including supervised learning, unsupervised learning, deep learning, and hybrid approaches. The survey provides insights into positioning applications related to GNSS such as signal analysis, anomaly detection, multi-sensor integration, prediction, and accuracy enhancement using ML. It discusses the strengths, limitations, and challenges of current ML-based approaches for GNSS positioning, providing a comprehensive overview of the field.  \nKeywords: GNSS, GPS, Machine Learning, Deep Learning, Survey  \n1 Introduction  \nGlobal Navigation Satellite Systems (GNSS)-based positioning underpins numerous essential applications, enabling efficiency, safety, and reliability across various industries. It serves a wide range of applications, including navigation, transportation, logistics, mapping, surveying, and precision agriculture, among others. Additionally, emergency services rely on GNSS for search and rescue operations. Maritime navigation, and aviation also heavily rely on GNSS for positioning information that  \n1  \nenhances situational awareness and reduces response times. Furthermore, GNSS plays a crucial role in synchronizing critical infrastructure systems such as power grids, telecommunication networks, and financial transactions [1, 2] .  \nHowever, GNSS measurements are subject to various sources of error that can affect positioning accuracy [3–5] . One source of error is signal interference that is caused by natural or man-made obstructions, such as tall buildings or dense foliage, leading to signal blockage, Non-Line-of-Sight (NLOS) errors, and multipath (MP) effects in urban environments. Another factor is atmospheric delays caused by the ionosphere and troposphere, which can influence the speed of the signals and introduce errors in distance measurements. Additionally, clock inaccuracies in both the satellites and receivers can contribute to errors in timing and positioning calculations. Other sources of error include satellite orbit inaccuracies and receiver noise. Mitigating these error sources is crucial in improving GNSS positioning performance for various applications.  \nTraditionally, model-based methods are used for GNSS positioning and error mitigation/detection because of the following advantages. Model-based methods incorporate knowledge about signal propagation characteristics in urban environments via statistical models that capture the characteristics of GNSS signals in urban environments. These models are based on well-understood physical principles, which have been refined and validated over decades, making their behavior predictable in different environments. Model-based algorithms are also less computationally intensive and do not necessarily need vast amounts of labeled data for training.  \nModel-based methods for GNSS positioning include Newton-Raphson [6], Weighted Least Squares (WLS) [1], and Kalman Filters [7] . While the Newton-Raphson method enables iterative refinement of the receiver’s position estimate [6, 8], WLS statistically optimizes the ","cbCaigiN437qffmH","https://ap.wps.com/l/cbCaigiN437qffmH","pdf",1644294,1,52,"English","en",105,"# Introduction\n## GNSS positioning applications and significance\n## Sources of GNSS measurement errors\n## Traditional model-based methods\n## Differential positioning and real-time/precision techniques","[{\"question\":\"Why do model-based GNSS positioning methods face limitations?\",\"answer\":\"They can lack adaptability to uncertain noise models and often underperform in challenging environments where signal conditions deviate from assumptions.\"},{\"question\":\"What ML approaches are covered for improving GNSS positioning?\",\"answer\":\"The survey covers supervised learning, unsupervised learning, deep learning, and hybrid ML approaches tailored to GNSS-related tasks.\"},{\"question\":\"Which GNSS positioning application areas does the survey emphasize?\",\"answer\":\"It highlights signal analysis, anomaly detection, multi-sensor integration, prediction, and accuracy enhancement using ML.\"}]","A Survey of Machine Learning Techniques for Improving Global Navigation Satellite Systems - 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