GNSS Reflectometry from Smartphones: Testing Performance of In-Built Antennas and GNSS Chips

Kurum, Mehmet; Gürbüz, Ali Cafer; Farhad, Mehedi

doi:10.1109/igarss39084.2020.9324124

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…During the experiment campaign, we also tested the performance of the smartphone in-built antennas. The result of such a study was reported in another study [40], and the conclusion was that the radiation pattern of the smartphone's GNSS antenna is observed to be highly irregular. Thus, we decided to use custom GNSS receivers for the UGV experiments since the irregular antenna pattern can cause discrepancies between reference and mobile receiver measurements, even if a gimbal system is utilized.…”

Section: Instrumentationmentioning

confidence: 81%

“…Recently, a new methodology called the Global Navigation Satellite System (GNSS) Transmissometry (GNSS-T) technique has gained attention to monitoring VWC directly. The method uses stationary [35]- [39] or mobile [40], [41] GNSS receivers under the forest canopy, accompanied by an additional stationary unit in the open area as reference. The logarithmic difference between the two measurements yields information on forest transmissivity (water content).…”

Section: Introductionmentioning

confidence: 99%

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A UGV-Based Forest Vegetation Optical Depth Mapping Using GNSS Signals

Ghosh,

Farhad,

Boyd

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this study, we conducted an empirical investigation on mobile Global Navigation Satellite System (GNSS) Transmissometry (GNSS-T) measurements to explore vegetation optical depth (VOD). Our approach involved using a dualreceiver setup, with one receiver located in open terrain to capture direct signals as a reference and another deployed on an unmanned ground vehicle (UGV) to sample vegetation across expansive forested regions. Noteworthy findings reveal the negligible influence of ground multipath effects within these forested terrains, effectively resulting in sampling the forest canopy rather than the ground itself as the receiver moves. The UGV-based method also uncovers VOD fluctuations inside the forest, offering insights into spatial distribution and the influence of satellite position on VOD measurements. The study further examines the effect of tree heterogeneity and seasonal dynamics on the VOD estimates. This empirical study contributes to our understanding of the VOD mapping capabilities of the mobile GNSS-T approach and can potentially lead to non-intrusive quantification of Vegetation Water Content (VWC) at a landscape scale in forest terrains. These results are significant for advancing our knowledge of forest ecosystem dynamics and sustainable resource management.

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Section: Instrumentationmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

A UGV-Based Forest Vegetation Optical Depth Mapping Using GNSS Signals

Ghosh,

Farhad,

Boyd

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…2 (b). The built-in antenna of the smartphone is highly irregular, so the gimbal keeps the smartphone at a fixed orientation and level regardless of drone's pitch, yaw and roll movement [49]. The metal mesh plate works as an electromagnetic shield that isolates the direct GPS signal from the reflected one.…”

Section: B Materials and Hardwarementioning

confidence: 99%

Fusion of Reflected GPS Signals With Multispectral Imagery to Estimate Soil Moisture at Subfield Scale From Small UAS Platforms

Senyurek

Farhad

Gürbüz

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This study proposes a low-cost and "proof-of-1 concept" methodology to obtain high spatial resolution soil 2 moisture (SM) via processing reflected Global Positioning System 3 (GPS) and a multispectral camera data acquired by small 4 Unmanned Aircraft System (UAS) platforms. An SM estimation 5 model is developed using a random forest (RF) machine-learning 6 (ML) algorithm by combining features obtained from reflected 7 GPS signals (collected by smartphones and commercial off the 8 shelf receivers) in conjunction with ancillary vegetation indices 9 from the multispectral camera data. The proposed ML algorithm 10 uses in-situ SM measurements acquired via SM probes as labels. 11 A preliminary field experiment was conducted on 210 m by 110 m 12 (2.31 ha) crop fields (corn and cotton) in 2020 (from January 13 to November, including crop planting through senescence time 14 period) at Mississippi State University (MSU)'s the heavily 15 instrumented North Farm to acquire data needed for the ML 16 model to train and test. Our results showed that both fields 17 could be covered by GPS reflectometry measurements with about 18 13 minutes of flight time at a 15-m altitude, and SM can be 19 mapped with 5m × 5m spatial resolution (corresponding to 20 the elongated first Fresnel zone). The model is trained with 21 and validated against eight in-situ SM station datasets via 10-22 fold and leave-one-probe-out cross-validation techniques. Overall 23 root-mean-square errors (RMSE) of 0.013 m 3 m −3 volumetric SM 24 and R-value of 0.95 [-] are obtained for 10-fold cross-validation. 25 The proposed model reached an RMSE of 0.033 m 3 m −3 and an 26 R-value of 0.5 [-] in leave-one-probe-out cross-validation. While 27 having limited data, the results indicate that high resolution SM 28 measurement can be achieved with a low-cost GPS reflectometry 29 system onboard a small UAS platform for use in precision 30 agriculture (PA) applications.31

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“…Therefore, many mountaineers now use their smartphones to navigate when climbing mountains, eliminating the need to read maps. However, the accuracy of GPS on a smartphone is unclear [ 4 ], because it is based on satellite position corrections sent from the nearest mobile phone base station [ 5 ], and these corrections are used to calculate the exact position of the smartphone. Map applications are designed to be used online and cannot be used offline.…”

Section: Introductionmentioning

confidence: 99%

High Precision Location Estimation in Mountainous Areas Using GPS

Kunisada¹,

Premachandra²

2022

Sensors

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Outdoor recreation has become popular in recent years, against the backdrop of the new coronavirus epidemic that started in 2020. Mountaineering, in particular, has become a popular pastime for many people as an easy way to experience nature. However, the number of mountaineering accidents is increasing, owing to the inadequate knowledge and equipment for beginners. In particular, the lack of map-reading skills and experience often leads to the selection of wrong trails. The smartphones used for precise location information obtain correction information from radio waves from a base station, and the accuracy of using only the GPS in mountainous areas without radio waves is questionable. In general, the GPS position correction methods in the literature for such situations include complex processing of the GPS radio waves. Some of these methods have been proposed with complex hardware and are difficult to implement with portable hardware. In this study, we develop and demonstrate a method for obtaining accurate location information using GPS without the error correction of radio waves, even in mountainous areas. The multipath is the reason for most of the GPS errors in the mountains. In the mountains, depending on the locations, the correct GPS location can also be received. In the proposed method, the correct GPS data are used to detect the incorrect GPS locations. We present an experimental method for estimating the interrelationship between the GPS longitude and latitude data. Additionally, we demonstrate the effectiveness of our method by showing that the experimental mountain location data presented in this paper are more accurate than the GPS data alone.

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GNSS Reflectometry from Smartphones: Testing Performance of In-Built Antennas and GNSS Chips

Cited by 5 publications

References 8 publications

A UGV-Based Forest Vegetation Optical Depth Mapping Using GNSS Signals

A UGV-Based Forest Vegetation Optical Depth Mapping Using GNSS Signals

Fusion of Reflected GPS Signals With Multispectral Imagery to Estimate Soil Moisture at Subfield Scale From Small UAS Platforms

High Precision Location Estimation in Mountainous Areas Using GPS

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