A specifically designed machine learning algorithm for GNSS position time series prediction and its applications in outlier and anomaly detection and earthquake prediction

Shahvandi, Mostafa Kiani

doi:10.48550/arxiv.2006.09067

2020

DOI: 10.48550/arxiv.2006.09067

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A specifically designed machine learning algorithm for GNSS position time series prediction and its applications in outlier and anomaly detection and earthquake prediction

Mostafa Kiani Shahvandi¹

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2024

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A survey of machine learning techniques for improving Global Navigation Satellite Systems

Mohanty,

Gao

2024

EURASIP J. Adv. Signal Process.

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Global 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, utilizing satellite geometry and the known properties of satellite signals. However, 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.

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A survey of machine learning techniques for improving Global Navigation Satellite Systems

Mohanty,

Gao

2024

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

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A specifically designed machine learning algorithm for GNSS position time series prediction and its applications in outlier and anomaly detection and earthquake prediction

Cited by 1 publication

References 17 publications

A survey of machine learning techniques for improving Global Navigation Satellite Systems

A survey of machine learning techniques for improving Global Navigation Satellite Systems

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