GIRAS: an open-source MATLAB-based software for GNSS-IR analysis

Altuntaş, Cemali; Tunalıoğlu, Nursu

doi:10.1007/s10291-021-01201-3

Cited by 12 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So, this study considers an outlier detection step for all the inversion results. Outliers are defined as points within the sliding window that differ from the local median by more than 3 times of the local scaled Median Absolute Deviation (MAD; Altuntas & Tunalioglu, 2022; https://se.mathworks.com/help/matlab/ref/rmoutliers.html). In this study, the rmoutliers function is used, and after sorting the time series of the inversion sea level height, every 10 data is a sliding window to remove outliers.…”

Section: Sea Level Retrieval and Reconstruction Approachmentioning

confidence: 99%

Hourly Sea Level Prediction‐Based GNSS‐IR Inversions by Combining the Least Squares Learning Cross‐Checking Method With the Gaussian Kernel Model L2 Constraint and LSTM

Zheng

Chai

et al. 2023

Earth and Space Science

View full text Add to dashboard Cite

Multisatellite systems and multi‐signal‐to‐noise ratio types provide a more prosperous data basis for the inversion of sea level by GNSS‐IR technology. However, there are few studies on data reconstruction of the retrieved sea level height. This study takes the SPBY station as an example and introduces the least squares learning cross‐checking method with the Gaussian kernel model (GKM) L2 constraint. Furthermore, research on hourly sea level reconstruction based on GNSS‐IR is carried out. Compared with the measured sea level, the sea level height in 2021 obtained after the fusion of multisource data has an R of 0.905 and an RMSE of 0.144 m. And the result obtained after data reconstruction has an R of 0.958 and an RMSE of 0.090 m. Compared with the multisource data fusion results before reconstruction, R is increased by 5.9%, and RMSE is decreased by 37.5%. Finally, using the reconstructed sea level data based on Long Short‐Term Memory (LSTM) artificial neural network to carry out the research on sea level prediction, which verifies the conclusion that more reliable forecast values can be obtained based on 5 months of training data. Among them, the R from 1 to 24 hr is 0.905, and the RMSE is 0.145 m. Compared with the inversion accuracy of GNSS‐IR, the R is increased by 2.0%, and the RMSE is decreased by 21.4%. This study demonstrates the feasibility of GNSS‐IR technology and L2‐constrained least squares learning cross‐checking method based on the GKM to reconstruct sea level data with high temporal resolution and high accuracy. The reliability of the sea level prediction based on GKM reconstruction and LSTM is verified in the sea level forecast of the next 24 epochs, which has essential applications in sea level data recovery and forecasting.

show abstract

Section: Sea Level Retrieval and Reconstruction Approachmentioning

confidence: 99%

Hourly Sea Level Prediction‐Based GNSS‐IR Inversions by Combining the Least Squares Learning Cross‐Checking Method With the Gaussian Kernel Model L2 Constraint and LSTM

Zheng

Chai

et al. 2023

Earth and Space Science

View full text Add to dashboard Cite

show abstract

“…The observed and simulated results show ionosphere sounding by GNSS TEC is a powerful tool to detect the natural hazard, which can serve as an important decision support for hazard warning and damage mitigation. Currently, there are many open-source programs for GNSS positioning and related applications (Nievinski and Larson 2014;Bahadur and Nohutcu 2018;Zhao et al 2021;Altuntas and Tunalioglu 2022). However, open-source program on detection of natural hazard by GNSS TEC is very scarce.…”

Section: Introductionmentioning

confidence: 99%

IonKit-NH: a MATLAB-based toolkit for ionospheric detection of natural hazard

Tang

2023

Preprint

View full text Add to dashboard Cite

In recent years, ionospheric sounding by GNSS total electron content (TEC) has been a powerful tool to detect the natural hazard such as earthquake, tsunami and volcanic eruption due to the fact that the natural hazard-produced acoustic waves or gravity waves can travel into the ionosphere and trigger travelling ionospheric disturbances (TIDs).However, open-source program on this field is scarce. Here, we developed a MATLAB-based toolkit, called IonKit-NH, to detection of natural hazard. IonKit-NH is capable of processing multi-system GNSS including GPS, GLONASS, Galileo and BDS and forming different dual-frequency combinations to solve TEC. Based on the GNSS TEC, IonKit-NH generates the time-distance map and two-dimensional map of TEC variation, which can be used to obtain the characteristics of TIDs induced by natural hazard.

show abstract

“…Recently, lowcost and compact GNSS-IR sensors have been developed for convenient environmental monitoring [14], [15], [16]. Moreover, the accessibility of the GNSS-IR technique has benefited from the emergence of various open-source software tools for processing GNSS-IR data [17], [18], [19]. A technique for precisely monitoring water levels using multiple co-located low-cost antennas was presented in [16].…”

mentioning

confidence: 99%

Observations of River Ice Breakup Using GNSS-IR, SAR, and Machine Learning

Purnell,

Dabboor,

Matte

et al. 2024

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

Global Navigation Satellite System-Interferometric Reflectometry (GNSS-IR) is an emerging sensor technique that has become well-established for water level monitoring. While GNSS-IR has previously been employed for monitoring properties of lake ice and sea ice, it has not been applied for monitoring river ice. This paper presents results from monitoring river ice breakup at three sites in Canada. GNSS-IR data was compared to co-located time-lapse camera imagery and it was found that GNSS-IR signal was sensitive to periods where there is rough or broken ice in view of the sensor. Using data from Sentinel-1 and the RADARSAT Constellation Mission (RCM), the first ever comparison of GNSS-IR with Synthetic Aperture Radar (SAR) imagery is presented and a negative correlation of -0.8 is found between the GNSS-IR spectral power and SAR backscatter. Three classification algorithms of varying complexity (K-means clustering, neural network and random forest) are explored for detecting river ice using GNSS-IR. Using a shallow neural network with two hidden layers, an optimal accuracy of up to 94% is achieved over all three sites, or 97% when mixed water-ice conditions are excluded from the analysis. In summary, GNSS-IR has strong potential for ice monitoring applications, including monitoring the formation of ice jams.

show abstract

GIRAS: an open-source MATLAB-based software for GNSS-IR analysis

Cited by 12 publications

References 29 publications

Hourly Sea Level Prediction‐Based GNSS‐IR Inversions by Combining the Least Squares Learning Cross‐Checking Method With the Gaussian Kernel Model L2 Constraint and LSTM

Hourly Sea Level Prediction‐Based GNSS‐IR Inversions by Combining the Least Squares Learning Cross‐Checking Method With the Gaussian Kernel Model L2 Constraint and LSTM

IonKit-NH: a MATLAB-based toolkit for ionospheric detection of natural hazard

Observations of River Ice Breakup Using GNSS-IR, SAR, and Machine Learning

Contact Info

Product

Resources

About