Navigation Context Adaptive Fault Detection and Exclusion Strategy based On Deep Learning &amp; Information Theory: Application to a GNSS/IMU Integration

In terms of the availability and accuracy of positioning, navigation, and timing (PNT), the traditional Global Navigation Satellite System (GNSS) algorithms and models perform well under good signal conditions. In order to improve their robustness and performance in less than optimal signal environments, many researchers have proposed machine learning (ML) based GNSS models (ML models) as early as the 1990s. However, no study has been done in a systematic way to analyze the extent of the research on the utilization of ML models in GNSS and their performance. The aim of this research is to perform a systematic review of the type of ML models utilized in GNSS use cases, their performance with respect to accuracy, their comparison with other models (ML and non-ML), and their GNSS application context. In this study, we perform a systematic review of studies from 2000 to 2021 in the literature that utilizes machine learning techniques in GNSS use cases. We assess the performance of the machine learning techniques in the existing literature on their application to GNSS. Furthermore, the strengths and weaknesses of machine learning techniques are summarized. In this paper, we have identified 213 selected studies and ten categories of machine learning techniques. The results prove the acceptable performance of machine learning

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Navigation Context Adaptive Fault Detection and Exclusion Strategy based On Deep Learning & Information Theory: Application to a GNSS/IMU Integration

Cited by 4 publications

References 31 publications

Contributions to the development of safe and accurate localisation solutions: The LOCSP project

Contributions to the development of safe and accurate localisation solutions: The LOCSP project

Environment Adaptive Diagnostic Framework For Safe Localization of Autonomous Vehicles

A Systematic Review of Machine Learning Techniques for GNSS Use Cases

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