Feasibility of retrieving effective reflector height using GNSS-IR from a single-frequency android smartphone SNR data

Altuntaş, Cemali; Tunalıoğlu, Nursu

doi:10.1016/j.dsp.2021.103011

Cited by 13 publications

(6 citation statements)

References 32 publications

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“…It can be found that there is no satellite trajectory in the south of the station because it is located in the Southern Hemisphere of the Earth. The plots represent the azimuth range, elevation angle, and coverage area (Altuntas & Tunalioglu, 2021). Sky plots showing the satellite tracks for GPS (G), GLONASS (R), Galileo (E), and BDS (C) on DOY68, 2021.…”

Section: 6 M mentioning

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

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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.

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Section: 6 M mentioning

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

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“…While the polarization mode of the smart electronic device antenna is linear polarization, so the impact of multipath signals cannot be avoided (Altuntas and Tunalioglu 2021). Based on the data collected in 5 d, it is planned to carry out the comparison experiment of the height measurement accuracy of the three to verify the height performance of the smart device.…”

Section: Station Environmentmentioning

confidence: 99%

Preliminary inquiry on the linear relationship between the height of the station and the ground height error retrieved by GNSS-IR with low-cost smart electronic equipment

Zheng,

Chai

2023

Meas. Sci. Technol.

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Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) ground height retrieval technology is based on global navigation satellite system (GNSS) signal reflection, which can achieve efficient and high-precision ground retrieval. However, errors cannot be avoided. And whether there is a linear relationship between the height of the station and the error is unknown. This research uses Hi-Target geodetic GNSS receivers, smart phone devices (Honor 60) and smart tablet devices (Huawei MatePad Pro) to collect a total of 5 d data from DOY65 to DOY69 in 2023, with the station heights of 0.8 m, 1.0 m, 1.2 m, 1.4 m and 1.6 m, respectively. The experimental results show that each satellite can effectively establish a linear relationship between the inversion error and the station height, which can be used in the error compensation research of different station heights under the limitation that the height of reflector is between 0.8 m and 1.6 m. Simultaneously, the error is related to the influence of comprehensive factors such as reflector type, satellite number, and data-receiving equipment. Secondly, two clustering methods, k-means and k-media, are introduced to cluster a and b in the linear relationship y = ax + b of each satellite, and it is proved that the linear relationship between inversion error and station height is obviously related to ground reflection surface (plastic track and concrete ground). Finally, it is verified that the height measurement accuracy of low-cost smart electronic equipment (Root Mean Square Error (RMSE): 0.047 m and 0.042 m) is worse than that of GNSS (RMSE: 0.010 m), but it still has good measurement performance. All in all, this study provides an essential technical reference for the error compensation of different station heights and for the application of GNSS-IR with low-cost smart electronic equipment. Due to its low-cost advantage, it has great potential in developing other surface parameter inversion of GNSS-IR technology.

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“…At the same time, GNSS-IR research based on smart mobile devices has a feasible and technical foundation, Strandberg J [5] , Altuntas C [6] The study demonstrated that Android devices such as tablets and smartphones performed on par with conventional measured GNSS receivers, with the advantage of utilizing signal-to-noise ratio(SNR) data at a higher altitude angle (>30°).In 2020,Strandberg and Haas collected GNSS SNR data for short-term observation times through tablets and showed that sea level change could be tracked by implementing GNSS-IR methods, showing that sea level height could be measured using mobile devices such as tablets and mobile phones, and that the accuracy of the experimental results was similar to that of more expensive geodetic devices [5] .In 2022, Li Jie, Yang Pengyu and others used the linear polarization antenna equipped with Android smartphones to collect GNSS-IR raw signal-to-noise ratio data, and successfully inverted the vertical distance between the lake water surface and the smartphone, verifying the availability of water surface height inversion based on smartphone GNSS-IR observation data [7] . Therefore, in this paper, in order to study the effect of different reflective surfaces on the accuracy of low-cost GNSS-IR receiver height inversion, the feasibility and effectiveness of low-cost receiver height inversion on different reflective surfaces are analyzed by comparing the difference between the inverted height and the measured height on different materials and the characteristics of the signal-to-noise ratio on different reflective surfaces.…”

Section: Introductionmentioning

confidence: 99%

Analysis of altitude inversion accuracy of various reflector receivers based on global navigation satellite system interferometry reflectometry

Ma,

Wang,

Yang

et al. 2024

Second International Conference on Environmental Remote Sensing and Geographic Information Technology (ERSGIT 2023)

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Global Navigation Satellite System Interferometry Reflectometry (GNSS-IR) technology is widely used in different fields. In order to realize the dynamic change monitoring of water surface height and snow depth based on GNSS-IR, it is very important to study the Global Navigation Satellite System (GNSS) signal reflection characteristics of the base surface when the height/depth is zero. At the same time, exploring the differences in the inversion accuracy of different materials on the ground can also provide a basis for the extended application of GNSS-IR technology. In this paper, a smart phone is used to conduct GNSS-IR altitude inversion accuracy experiments on the plastic surface, water surface, cement surface and soil surface respectively. The root mean square error is the largest, which is 4.15cm, which proves that different reflectors have an impact on the accuracy of GNSS-IR height retrieval, and experiments have proved that the accuracy of the retrieval results is the highest when the observation value is in the range of 10°-60° elevation angle.

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Feasibility of retrieving effective reflector height using GNSS-IR from a single-frequency android smartphone SNR data

Cited by 13 publications

References 32 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

Preliminary inquiry on the linear relationship between the height of the station and the ground height error retrieved by GNSS-IR with low-cost smart electronic equipment

Analysis of altitude inversion accuracy of various reflector receivers based on global navigation satellite system interferometry reflectometry

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