The Satellite Selection Algorithm of GNSS Based on Neural Network

Wei, Jinben; Li, Kezhao; Zhao, Lijun; Wang, Yunkai; Li, Zhiwei

doi:10.1007/978-981-10-0934-1_11

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2020

2024

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Cited by 3 publications

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“…The reason lied in that most GNC receivers today could be able to track satellite signals fairly more than four satellites. The method of satellite selection by using Generalized Regression Neural Network (GRNN) was proposed [7]. In addition, statistics and machine learning methods, such as Support Vector Machine (SVM), Pace Regression (PR), Artificial Neural Network (ANN) and Genetic Programming (GP), were adopted to process the approximation calculation of GDOP and indicated that SVM and GP could achieve better performance than other algorithms [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Identification and Exclusion Multiple Outliers in GNC Microsystem

Meng¹,

Xing²,

Sun³

et al. 2020

AJSET

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Section: Literature Reviewmentioning

confidence: 99%

Identification and Exclusion Multiple Outliers in GNC Microsystem

Meng¹,

Xing²,

Sun³

et al. 2020

AJSET

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Unsupervised Clustering of GNSS Receiver Measurements for Satellite Selection

Savas

2023

2023 10th International Conference on Recent Advances in Air and Space Technologies (RAST)

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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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The Satellite Selection Algorithm of GNSS Based on Neural Network

Cited by 3 publications

References 4 publications

Identification and Exclusion Multiple Outliers in GNC Microsystem

Identification and Exclusion Multiple Outliers in GNC Microsystem

Unsupervised Clustering of GNSS Receiver Measurements for Satellite Selection

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

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