{'fraction_non_alphanumeric': 0.016356877323420074, 'fraction_numerical': 0.0, 'mean_word_length': 5.146118721461187, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Machine Learning Utilization in GNSS—Use Cases, Challenges and Future Applications

The algorithms and models of traditional global navigation satellite systems (GNSSs) perform very well in terms of the availability and accuracy of positioning, navigation and timing (PNT) under good signal conditions. Research is still ongoing to improve their robustness and performance in less than optimal signal environments. A growing interest in the study of machine learning (ML) and the potential for its application in many fields has also led to several types of research on its utilization in GNSSs. In the field of GNSSs, ML is changing the ways that navigation problems are prevented and resolved, and it is taking on a significant role in advancing PNT technologies for the future. We illustrate this point by reviewing how ML can enhance GNSS performance and usability and also discuss areas of GNSSs in which ML algorithms have been applied. We also highlight the commonly implemented ML algorithms and compare their performance when used in similar GNSS use cases. In addition, the challenges and risks of the utilization of ML techniques in GNSSs are discussed. Insight is given into prospective areas in GNSSs in which ML can be applied for increased performance, accuracy and robustness, thereby providing fertile ground for novel research.