Feasibility of Wind Direction Observation Using Low-Altitude Global Navigation Satellite System-Reflectometry

Wang, Feng; Yang, Dongkai; Yang, Lei

doi:10.1109/jstars.2018.2877388

Cited by 12 publications

(9 citation statements)

References 32 publications

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“…The resolution can be determined by the Formula (7) in the range and azimuth direction. Combined with the power analysis of the navigation signal backscatter [ 34 ], the subset of navigation satellites for imaging can be constrained by constraining its azimuth angle

and elevation angle

.…”

Section: Improved Dual-satellite Fusion Imaging Methodsmentioning

confidence: 99%

Improved GNSS-Based Bistatic SAR Using Multi-Satellites Fusion: Analysis and Experimental Demonstration

Yang

Zhu

et al. 2020

Sensors

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The Global Navigation Satellite System (GNSS)-based Bistatic Synthetic Aperture Radar (SAR) is getting more and more attention in remote sensing for its all-weather and real-time global observation capability. Its low range resolution results from the narrow signal bandwidth limits in its development. The configuration difference caused by the illumination angle and movement direction of the different satellites makes it possible to improve resolution by multi-satellite fusion. However, this also introduces new problems with the resolution-enhancing efficiency and increased computation brought about by the fusion. In this paper, we aim at effectively improving the resolution of the multi-satellite fusion system. To this purpose, firstly, the Point Spread Function (PSF) of the multi-satellite fusion system is analyzed, and focusing on the relationship between the fusion resolution and the geometric configuration and the number of satellites. Numerical simulation results show that, compared with multi-satellite fusion, dual-satellite fusion is a combination with higher resolution enhancement efficiency. Secondly, a method for dual-satellite fusion imaging based on optimized satellite selection is proposed. With the greedy algorithm, the selection is divided into two steps: in the first step, according to geometry configuration, the single-satellite with the optimal 2-D resolution is selected as the reference satellite; in the second step, the angles between the azimuthal vector of the reference satellite and the azimuthal vector of the other satellites were calculated by the traversal method, the satellite corresponding to the intersection angle which is closest to 90° is selected as the auxiliary satellite. The fused image was obtained by non-coherent addition of the images generated by the reference satellite and the auxiliary satellite, respectively. Finally, the GPS L1 real orbit multi-target simulation and experimental validation were conducted, respectively. The simulation results show that the 2-D resolution of the images produced by our proposed method is globally optimal 15 times and suboptimal 8 times out of 24 data sets. The experimental results show that the 2-D resolution of our proposed method is optimal in the scene, and the area of the resolution unit is reduced by 70.1% compared to the single-satellite’s images. In the experiment, there are three navigation satellites for imaging, the time taken to the proposed method was 66.6% that of the traversal method. Simulations and experiments fully demonstrate the feasibility of the method.

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and elevation angle

.…”

Section: Improved Dual-satellite Fusion Imaging Methodsmentioning

confidence: 99%

Improved GNSS-Based Bistatic SAR Using Multi-Satellites Fusion: Analysis and Experimental Demonstration

Yang

Zhu

et al. 2020

Sensors

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“…where W D is the wind direction; σ 2 up and σ 2 cross are the mean surface slopes (MSSs) of alone up-and cross-wind, and can be estimated from the ocean wave spectrum by integration. The Elfouhaily's unified spectrum [27] parameterized on the wind speed considers the influence of fetch and has been widely used in GNSS-R [28], [29], therefore, it is also used in this paper. According to [30], the GNSS signal is sensitive to surface wave at ∼ 50 cm scale in wavelength and longer.…”

Section: Delayed Waveformsmentioning

confidence: 99%

Retrieval of Ocean Wind Speed Using Airborne Reflected GNSS Signals

et al. 2019

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This paper develops the models that can be used in different flight scenarios to retrieve the wind speed using the width of the normalized delay waveform (NDW). First, the factors that influence the NDW width, including the wind speed, wind direction, flight height, and elevation angle, are analyzed. The contribution of each independent variable to the regression is explored. The results show that the wind speed, flight height, and elevation angle contribute more significantly than the wind direction so the wind direction can be ignored in the model. The multiple regression, in which the function terms of NWD width, flight height, and elevation angle above are taken as independent variables and wind speed is taken as the dependent variable, is proposed to develop the model of retrieving wind speed. Through the simulation, a root-mean-square error (RMSE) over 3 m/s can be obtained. In order to improve the retrieval performance, a Back-Propagation (BP) network is trained as an alternative to the analytical models above. Better performance is achieved with an RMSE less than 2.5 m/s under the same conditions with the analytical model. The errors of retrieved wind speed are analyzed. The conclusions are that: 1) both analytical model and BP network have inherent regression biases, especially for high wind speed from 15 to 20m/s so that at wind speeds higher than 16 m/s, the tendency for retrieval accuracy to rapidly become worse appears and 2) the number of incoherent averaging should be over 1000 to reduce the impact of thermal and speckle noise. At the end of the paper, airborne data are processed to retrieve wind speed utilizing proposed models and the matching method to compare in-situ wind speed from the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS). By comparing the retrieval results, the proposed methods could obtain the accuracy with the same level of matching method.INDEX TERMS Wind speed, reflected GNSS signal, normalized delay waveform (NDW), NDW width, multiple regression, neural network.

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“…For more information, see http://creativecommons.org/licenses/by/4.0/ away from the specular region [25]. A wind direction retrieval algorithm using low-altitude GNSS-R in backward geometry was proposed and examined using the simulated data [26]. The disadvantage of the method using backward scattered GNSS signals is low-power level of the received signals, particularly for spaceborne GNSS-R, although it has been confirmed that backward scattered GNSS signals could be received from the airborne platform in [26].…”

Section: Introductionmentioning

confidence: 99%

“…A wind direction retrieval algorithm using low-altitude GNSS-R in backward geometry was proposed and examined using the simulated data [26]. The disadvantage of the method using backward scattered GNSS signals is low-power level of the received signals, particularly for spaceborne GNSS-R, although it has been confirmed that backward scattered GNSS signals could be received from the airborne platform in [26]. Therefore, the sensitivity to wind direction and the power level of scattered signals should be compromised to select the appropriate scattering geometry of the observation, when attempting to retrieve wind direction using GNSS-R. Based on the previous description and the published research works mentioned above, it is more likely to retrieve wind direction from spaceborne GNSS-R observables using the slightly nonspecular observation geometry between the purely specular and backward.…”

Section: Introductionmentioning

confidence: 99%

Wind Direction Retrieval Using Spaceborne GNSS-R in Nonspecular Geometry

Yang

Wang

2020

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

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A wind direction retrieval algorithm using spaceborne global navigation satellite system reflectometry (GNSS-R) in nonspecular geometry is presented. This article first focuses on investigating the influence of wind direction on the scattering strength purely in specular geometry and nonspecular geometry. The sensitivity of the scattered GNSS signals in nonspecular geometry to wind direction is demonstrated despite the weaker power level. Therefore, a wind direction retrieval algorithm based on the geophysical model function (GMF) whose parameters are parameterized as a look-up-table is proposed. Then, the spatial resolution and power link of the spaceborne GNSS-R in nonspecular geometry are analyzed. The incidence angle of the scattered signals ranges 15 • to 35 • to ensure the sensitivity to wind direction and the power level of scattered signals. The retrieving results using simulated data show that the ambiguity of the measurement geometry can be eliminated by using the multisatellite observation, and two possible wind directions for the same corresponding observable are obtained in the wind direction retrieval algorithm. The accuracy of wind speed importantly impacts on the retrieving performance of wind direction, therefore, it is needed to improve the measuring accuracy of wind speed. These results provide information to guide the wind direction retrieval using spaceborne GNSS-R in nonspecular geometry.

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Feasibility of Wind Direction Observation Using Low-Altitude Global Navigation Satellite System-Reflectometry

Cited by 12 publications

References 32 publications

Improved GNSS-Based Bistatic SAR Using Multi-Satellites Fusion: Analysis and Experimental Demonstration

Improved GNSS-Based Bistatic SAR Using Multi-Satellites Fusion: Analysis and Experimental Demonstration

Retrieval of Ocean Wind Speed Using Airborne Reflected GNSS Signals

Wind Direction Retrieval Using Spaceborne GNSS-R in Nonspecular Geometry

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