Retrieving the SNR metrics with different antenna configurations for GNSS-IR

Yıl 2022, Cilt: 6 Sayı: 1, 87 - 94, 30.01.2022

Cemali Altuntaş Nursu Tunalıoğlu

https://doi.org/10.31127/tuje.870620

Cited By: 1

Öz

Multipath, which is a major source of error for precise positioning, is the effect that occurs when Global Navigation Satellite Systems (GNSS) signals reach the receiver by reflecting from one or more surfaces. Reflected signals affect the signal-to-noise ratio (SNR) data provided by the receiver, indicating the signal strength. The structure of the antenna of the receiver and the direction in which it is oriented also change the strength of the received signal. In this study, the effect of antenna orientation and polarization on SNR data was demonstrated by using the method called GNSS-Interferometric Reflectometry (GNSS-IR), in terms of reflector height estimates. A geodetic GNSS receiver (CHC i50) and two different smartphones (Xiaomi Mi8 and Xiaomi Mi8 Lite) were used in the four-day experiments. The geodetic receiver was established as zenith-looking (ZL) in the first two days and as horizon-looking (HL) in the last two days. Smartphones were placed on the same mast with the HL receiver in the last two days. It was seen that it is more appropriate to use a 0°-60° satellite elevation angle range in the common use of all receivers’ data. In the 30°-60° range where the ZL installation receives the multipath signals weakly, it has been found that the HL receiver and smartphones have reflector height estimation accuracies with values ranging from 1.9 cm to 2.5 cm. In short, for different elevation angle ranges, accuracies below 2 cm could be obtained with each receiver. Thus, different antenna configurations may be used in GNSS-IR studies, depending on the characteristics of the study area and the surface feature to be determined.

Anahtar Kelimeler

Multipath, Signal-to-noise ratio (SNR), GNSS Interferometric Reflectometry (GNSS-IR), Smartphone, Horizon-looking antenna

Kaynakça

• Altuntas C & Tunalioglu N (2020a). Estimation performance of soil moisture with GPS-IR method. Sigma Journal of Engineering and Natural Sciences, 38(4), 2217-2230.
• Altuntas C & Tunalioglu N (2020b). An experimental study on the effect of antenna orientation on GNSS-IR. 1st Intercontinental Geoinformation Days, pp. 244-247.
• Anderson K D (2000). Determination of water level and tides using interferometric observations of GPS signals. Journal of Atmospheric and Oceanic Technology. 17(8), 1118-1127, doi:10.1175/1520-0426(2000)017<1118:DOWLAT>2.0.CO;2.
• Bilich A, Larson K M & Axelrad P (2008). Modeling GPS phase multipath with SNR: Case study from the Salar de Uyuni, Boliva, Journal of Geophysical Research, 113, B04401, doi:10.1029/2007JB005194.
• Chen Q, Won D & Akos D M (2014). Snow depth sensing using the GPS L2C signal with a dipole antenna. EURASIP Journal on Advances in Signal Processing, 1(106). doi:10.1186/1687-6180-2014-106.
• Gutmann E D, Larson K M, Williams M W, Nievinski F G & Zavorotny V (2012). Snow measurement by GPS interferometric reflectometry: an evaluation at Niwot Ridge, Colorado. Hydrological Processes, 26(19), 2951-2961, doi:10.1002/hyp.8329.
• Han M, Zhu Y, Yang D, Chang Q, Hong X & Song S (2020). Soil moisture monitoring using GNSS interference signal: proposing a signal reconstruction method. Remote Sensing Letters, 11(4), 373-382. DOI: 10.1080/2150704X.2020.1718235.
• Hofmann-Wellenhof B, Lichtenegger H & Wasle E (2007). GNSS–global navigation satellite systems: GPS, GLONASS, Galileo, and more. Springer Science & Business Media.
• Jin S, Qian X & Kutoglu H (2016). Snow depth variations estimated from GPS-Reflectometry: A case study in Alaska from L2P SNR data. Remote sensing, 8(1), 63. doi:10.3390/rs8010063.
• Larson K M, Nievinski F G (2013). GPS snow sensing: results from the EarthScope Plate Boundary Observatory, GPS Solut (2013) 17:41–52, DOI 10.1007/s10291-012-0259-7.
• Larson K M, Small E E, Gutmann E D, Bilich A L, Braun J J & Zavorotny V U (2008). Use of GPS receivers as a soil moisture network for water cycle studies. Geophysical Research Letters, Vol. 35, L24405, doi:10.1029/2008GL036013.
• Larson K M, Gutmann E D, Zavorotny V U, Braun J J, Williams M W & Nievinski F G (2009). Can we measure snow depth with GPS receivers?. Geophysical Research Letters, 36(17), doi:10.1029/2009GL039430.
• Larson K M, Braun J J, Small E E, Zavorotny V U, Gutmann E D & Bilich A L (2010). GPS Multipath and Its Relation to Near-Surface Soil Moisture Content. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(1), 91-99. doi: 10.1109/JSTARS.2009.2033612.
• Leroy A M & Rousseeuw P J (1987). Robust regression and outlier detection. Wiley Series in Probability and Mathematical Statistics, New York.
• Martin-Neira M (1993). A passive reflectometry and interferometry system (PARIS): Application to ocean altimetry. ESA Journal, 17(4), 331-355.
• Ozeki M & Heki K (2012). GPS snow depth meter with geometry-free linear combinations of carrier phases. Journal of Geodesy, 86(3), 209-219, doi:10.1007/s00190-011-0511-x.
• Roussel N, Frappart F, Ramillien G, Darrozes J, Baup F, Lestarquit L & Ha M C (2016). Detection of Soil Moisture Variations Using GPS and GLONASS SNR Data for Elevation Angles Ranging From 2° to 70°. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9 (10), 4781.
• Tunalioglu N, Dogan A H, Durdag U M (2019). GPS sinyal gürültü oranı verileri ile kar kalınlığının belirlenmesi. HKMOJJD, 6(1) 1-9. Doi: 10.9733/JGG.2019R00601001.T.
• Xi R, Zhou X, Jiang W & Chen Q (2018). Simultaneous estimation of dam displacements and reservoir level variation from GPS measurements. Measurement, 122, 247-256. doi:10.1016/j.measurement.2018.03.036.
• Yang Y, Zheng Y, Yu W, Chen W & Weng D (2019). Deformation monitoring using GNSS-R technology. Advances in Space Research 63 (2019) 3303–3314.
• Zhang S, Roussel N, Boniface K, Ha M C, Frappart F, Darrozes J, Baup F & Calvet J C (2017). Use of reflected GNSS SNR data to retrieve either soil moisture or vegetation height from a wheat crop. Hydrol. Earth Syst. Sci., 21, 4767–4784, https://doi.org/10.5194/hess-21-4767-2017.