Hurricane Tracking Using Multi-GNSS-R and Deep Learning

Alshaye, Meshal; Alawwad, Faisal; Elshafiey, Ibrahim

doi:10.1109/iccais48893.2020.9096717

Cited by 14 publications

(6 citation statements)

References 10 publications

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“…More recently, several studies have shown that ANNs can also improve the accuracy of GNSS-R wind speed retrieval using groundbased and spaceborne data (Kasantikul et al, 2018;Liu et al, 2019;Gao et al, 2019a;Asgarimehr et al, 2019), which have shown promising performance by using data collected by the TDS-1 (Wang et al, 2018;Asgarimehr et al, 2019) and CYGNSS missions (Liu et al, 2019;Reynolds et al, 2020). Moreover, this approach has been also attempted in some other GNSS-R applications, such as sea ice detection (Yan and Huang, 2018), soil moisture (Feng et al, 2018;Eroglu et al, 2019), hurricane tracking (Alshaye et al, 2020), and inland water detection (Ghasemigoudarzi et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Analysis of coastal wind speed retrieval from CYGNSS mission using artificial neural network

Yang

et al. 2021

Remote Sensing of Environment

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Section: Introductionmentioning

confidence: 99%

Analysis of coastal wind speed retrieval from CYGNSS mission using artificial neural network

Yang

et al. 2021

Remote Sensing of Environment

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“…Hurricane tracking: ML has also been applied to hurricane tracking using a convolutional neural network (CNN) in [24]. The trained CNN regression model has achieved accuracy with less than 1.5 pixels errors in x and y coordinates, i.e.…”

Section: Earth Observation and Monitoringmentioning

confidence: 99%

A Systematic Review of Machine Learning Techniques for GNSS Use Cases

Siemuri

Selvan

Välisuo

et al. 2022

IEEE Trans. Aerosp. Electron. Syst.

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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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“…On another set of GNSS applications, the impact of the deep learning approaches to counteract GNSS spoofing [ 34 , 35 , 36 , 37 , 38 ] and jamming [ 39 , 40 ] attacks is presented in several works. In the context of the GNSS for Earth sciences, deep learning was considered for earthquake prediction [ 41 ], hurricane monitoring [ 42 ], ice detection [ 43 ], and ionospheric scintillation [ 44 , 45 , 46 ], as well as in the survey article in [ 47 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning of GNSS Acquisition

Borhani-Darian

et al. 2023

Sensors

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Signal acquisition is a crucial step in Global Navigation Satellite System (GNSS) receivers, which is typically solved by maximizing the so-called Cross-Ambiguity Function (CAF) as a hypothesis testing problem. This article proposes to use deep learning models to perform such acquisition, whereby the CAF is fed to a data-driven classifier that outputs binary class posteriors. The class posteriors are used to compute a Bayesian hypothesis test to statistically decide the presence or absence of a GNSS signal. The versatility and computational affordability of the proposed method are addressed by splitting the CAF into smaller overlapping sections, which are fed to a bank of parallel classifiers whose probabilistic results are optimally fused to provide a so-called probability ratio map from which acquisition is decided. Additionally, the article shows how noncoherent integration schemes are enabled through optimal data fusion, with the goal of increasing the resulting classifier accuracy. The article provides simulation results showing that the proposed data-driven method outperforms current CAF maximization strategies, enabling enhanced acquisition at medium-to-high carrier-to-noise density ratios.

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Hurricane Tracking Using Multi-GNSS-R and Deep Learning

Cited by 14 publications

References 10 publications

Analysis of coastal wind speed retrieval from CYGNSS mission using artificial neural network

Analysis of coastal wind speed retrieval from CYGNSS mission using artificial neural network

A Systematic Review of Machine Learning Techniques for GNSS Use Cases

Deep Learning of GNSS Acquisition

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