Sea surface wind direction unambiguous retrieval method and device based on space-borne GNSS-R

Abstract

This application discloses a method and apparatus for unambiguous sea surface wind direction inversion based on spaceborne GNSS-R, relating to the fields of microwave remote sensing and marine environmental monitoring. The method includes: acquiring GNSS-R observation data from paired spaceborne GNSS-R receivers; extracting the Doppler centroid feature observation values ​​from the receivers; inputting prior wind speed and incident angle into a constructed Doppler centroid feature function model to obtain predicted Doppler centroid feature values; constructing a joint cost function based on the relative azimuth deflection of the two receivers, the predicted Doppler centroid feature values, and the observed Doppler centroid feature values; adjusting the relative wind direction angle until the joint cost function reaches its minimum value to obtain the sea surface wind direction. This application solves the problems of poor sensitivity to wind direction features in deep time-delay regions and the multi-solution ambiguity caused by the inherent symmetry of single-satellite forward scattering geometry in existing technologies, thereby achieving high-precision, unique-solution sea surface wind direction inversion.

Description

Technical Field

[0001] This application relates to the fields of microwave remote sensing and marine environmental monitoring technology, and in particular to a method and device for unambiguous inversion of sea surface wind direction based on spaceborne GNSS-R. Background Technology

[0002] Global Navigation Satellite System-Reflectometry (GNSS-R), as an emerging bistatic radar remote sensing technology, has shown great application potential in the field of marine environmental monitoring (such as sea surface wind field, sea ice, sea surface height, etc.) due to its advantages such as abundant signal sources, all-weather operation, low power consumption, and high spatiotemporal resolution.

[0003] Currently, spaceborne GNSS-R receivers can generate a Delay-Doppler Map (DDM) by processing sea surface reflected signals, and further use the DDM to invert sea surface wind fields. However, compared to wind speed, the inversion of sea surface wind direction has always been limited by the underlying physical mechanisms, resulting in the lack of a reliable technical solution for wind direction inversion. Specifically, the relevant technologies mainly suffer from the following two major drawbacks: First, GNSS-R signals are extremely insensitive to wind direction characteristics, making it difficult to extract effective features with high fidelity. Under the mainstream mirror forward scattering geometry, the energy of the DDM is highly concentrated in the region near the specular reflection point. The scattering cross section in this region is mainly dominated by isotropic large-scale gravity waves, which are extremely insensitive to wind direction characteristics (especially the asymmetry dominated by capillary waves). Most existing DDM products crop the high-energy region at the center of the DDM (near the specular reflection point), resulting in extremely weak wind direction information at the edges (skirts) of the DDM. The extracted features have poor robustness and cannot support high-precision inversion requirements.

[0004] Second, the inherent symmetry of single-satellite observation geometry leads to severe ambiguity in wind direction inversion. Due to the spatial symmetry of the forward scattering geometry of a single GNSS-R satellite, the extracted wind direction feature parameters typically exhibit a cosine-like even function shape as the wind direction changes. Inversion algorithms based on wind direction features such as power amplitude information will inevitably produce extremely close local minima within the search space from 0 to 360 degrees. This inherent defect in the feature and geometric physical mechanism prevents conventional optimization algorithms from independently removing pseudo-minimas, resulting in ambiguity in the wind direction inversion results and severely hindering the operational application of GNSS-R wind measurement data.

[0005] Therefore, there is currently a lack of a method that can effectively utilize spaceborne GNSS-R to achieve high-precision, unique solution for sea surface wind direction inversion. Summary of the Invention

[0006] This application provides a fuzz-free method and apparatus for sea surface wind direction inversion using spaceborne GNSS-R, which solves the technical problems of poor sensitivity to wind direction characteristics in deep time-delay regions and multiple solutions to fuzzy wind direction inversion caused by the inherent symmetry of single-satellite forward scattering geometry in existing spaceborne GNSS-R wind measurement technology. This achieves high-precision, unique solution sea surface wind direction inversion. The technical solution is as follows: Firstly, this application provides a method for unambiguous inversion of sea surface wind direction based on spaceborne GNSS-R, including: Two spaceborne GNSS-R receivers within the same sea surface grid in the target sea area are identified for synchronous observation, and GNSS-R observation data are acquired respectively; the GNSS-R observation data includes time delay-Doppler mapping data, incident angle, and relative azimuth angle deflection between the receivers; Based on the time-delay-Doppler mapping data, obtain the Doppler centroid feature observation value of the corresponding receiver; based on the wind field observation data within the same time period, obtain the prior wind speed of the corresponding sea surface grid; Input the prior wind speed and incident angle into the constructed Doppler centroid feature function model, and calculate the corresponding Doppler centroid feature prediction values ​​of the receiver under different relative wind angles. A joint cost function is constructed based on the relative azimuth deflection of the two receivers, the predicted value of the Doppler centroid feature, and the observed value of the Doppler centroid feature. The value of the relative wind direction angle is adjusted until the joint cost function reaches its minimum value, and the relative wind direction angle corresponding to the minimum value is output. The sea surface wind direction of the sea surface grid is obtained by processing the output relative wind direction angle.

[0007] In one alternative embodiment of the first aspect, the construction process of the Doppler centroid feature function model includes the following steps: Acquire GNSS-R sample data for various time periods obtained from observations of the target sea area by multiple spaceborne GNSS-R receivers, and obtain wind field sample data matching each GNSS-R sample data based on wind field observation data within the corresponding time period; Based on the time-delay-Doppler mapping data in the GNSS-R sample data, the corresponding Doppler centroid feature observation values ​​are obtained; The sample azimuth and incident angle are extracted from the GNSS-R sample data, the sample wind direction and wind speed are extracted from the wind field sample data, and the sample relative wind direction angle is calculated based on the sample wind direction and sample azimuth. By fitting the Doppler centroid feature observations, relative wind direction angle, wind speed, and incident angle of multiple GNSS-R sample data, the mapping relationship between the Doppler centroid feature observations and the relative wind direction angle, wind speed, and incident angle is determined, and a Doppler centroid feature function model is constructed.

[0008] In one alternative approach of the first aspect, the steps for obtaining the corresponding Doppler centroid feature observations based on time-delay-Doppler mapping data processing include: The pure noise time delay interval of the time delay-Doppler mapping data is determined, and the noise floor reference is calculated based on the statistical mean of the scattered power within the pure noise time delay interval. Based on the noise floor benchmark, the time delay-Doppler mapping data is subjected to noise removal to obtain the denoised time delay-Doppler mapping data. The denoised time-delay-Doppler mapping data is normalized to obtain normalized time-delay-Doppler mapping data. The normalized time-delay-Doppler mapping data is processed by a preset noise reduction threshold mask. Pixels in the normalized time-delay-Doppler mapping data whose signal power is less than the noise reduction threshold corresponding to the noise reduction threshold mask are assigned a value of 0, thus obtaining the masked time-delay-Doppler mapping data. Doppler centroid feature observations were extracted from the time-delay-Doppler mapping data after masking, using the following formula: ; in, This represents the extracted Doppler centroid feature observations. For the time delay of the time-Doppler mapping data, For the corresponding delay-Doppler mapping data, the Doppler frequency, This represents the time-delay-Doppler mapping data after masking.

[0009] In one alternative scheme of the first aspect, if the total number of GNSS-R sample data is greater than a preset threshold, the signal quality parameters calculated based on the delay-Doppler mapping data of all GNSS-R sample data are greater than or equal to the signal-to-noise ratio threshold, and all GNSS-R sample data are uniformly distributed within the range of relative wind direction angle, wind speed, and incident angle, then the step of constructing the Doppler centroid feature function model further includes: The grid is divided based on the distribution of all GNSS-R sample data within the range of relative wind direction angle, wind speed and incident angle, and a three-dimensional feature grid with relative wind direction angle, wind speed and incident angle as feature dimensions is obtained. Fill the corresponding three-dimensional feature grid with the Doppler centroid feature observation value corresponding to each GNSS-R sample data; Probability density analysis is performed on the Doppler centroid feature observations in each three-dimensional feature grid. Abnormal outliers with deviations greater than a preset deviation threshold are removed. The mean of the remaining Doppler centroid feature observations is calculated and used as the Doppler centroid feature baseline value of the corresponding three-dimensional feature grid. Using relative wind direction angle, wind speed, and incident angle as independent variables, and the Doppler centroid feature benchmark value of each three-dimensional feature grid as the dependent variable, the mapping relationship between the independent and dependent variables is determined by multivariate nonlinear regression, thus constructing the Doppler centroid feature function model.

[0010] In one alternative to the first aspect, if the total number of GNSS-R sample data is less than or equal to a preset threshold, the step of constructing the Doppler centroid feature function model further includes: Configure the parameters of the spaceborne GNSS-R in the physical simulator, and perform simulation by adjusting the simulated wind direction angle and simulated wind speed through the physical simulator to obtain GNSS-R simulation data; The theoretical values ​​of the corresponding Doppler centroid characteristics are obtained by processing the theoretical time delay-Doppler mapping data of GNSS-R simulation data. Using the theoretical values ​​of the Doppler centroid features obtained from simulation as the dependent variable, and the relative wind direction angle, wind speed, and incident angle obtained from simulation as the independent variables, the mapping relationship between the dependent and independent variables is determined, and the initial Doppler centroid feature function model is constructed. The GNSS-R sample data are divided into a calibration set and a validation set. The relative wind direction angle, wind speed and incident angle of each GNSS-R sample data in the calibration set are input into the initial Doppler centroid feature function model to obtain the Doppler centroid feature mapping value. The model parameters of the initial Doppler centroid feature function model are adjusted based on the difference between the Doppler centroid feature mapping value and the Doppler centroid feature observation value of each GNSS-R sample data in the calibration set, so as to obtain the calibrated Doppler centroid feature function model.

[0011] In one alternative embodiment of the first aspect, after obtaining the calibrated Doppler centroid eigenfunction model, the method further includes: Input the relative wind direction angle, wind speed and incident angle of each GNSS-R sample data in the validation set into the calibrated Doppler centroid feature function model to obtain the predicted value of the Doppler centroid feature. The root mean square error is calculated based on the predicted and observed Doppler centroid features of each GNSS-R sample data in the validation set. If the root mean square error is less than the preset error threshold, the calibrated Doppler centroid feature function model is verified and the calibrated Doppler centroid feature function model is output. Otherwise, adjust the simulation parameters of the physics simulator and re-execute the steps for configuring the parameters of the spaceborne GNSS-R in the physics simulator until the calibrated Doppler centroid characteristic function model is verified.

[0012] In one alternative of the first aspect, the joint cost function is constructed based on the difference between the predicted and observed Doppler centroid features of the two receivers, as expressed by the formula: ; In this context, subscript A represents the reference receiver among the two spaceborne GNSS-R receivers used for synchronous observation, and subscript B represents the other receiver. The relative wind direction angle to be solved; This indicates the relative azimuth deflection. This represents the Doppler centroid characteristic observation value of the reference receiver. This represents the Doppler centroid feature observation value of another receiver; Indicates the angle of incidence of the reference receiver. Indicates the angle of incidence of the other receiver; Indicates the prior wind speed; This represents the constructed Doppler centroid eigenfunction model. This represents the predicted Doppler centroid feature value corresponding to the reference receiver. This represents the predicted Doppler centroid feature value corresponding to the other receiver.

[0013] Secondly, this application also provides a sea surface wind direction unambiguous inversion device based on spaceborne GNSS-R, comprising: The data acquisition unit is used to identify two spaceborne GNSS-R receivers in the same sea surface grid within the target sea area for synchronous observation, and to acquire GNSS-R observation data respectively; the GNSS-R observation data includes time delay-Doppler mapping data, incident angle information, and relative azimuth angle deflection between the receivers; The data processing unit is used to obtain the Doppler centroid feature observation value of the corresponding receiver based on the time delay-Doppler mapping data; and to obtain the prior wind speed of the corresponding sea surface grid based on the wind field observation data within the same time period. The wind direction inversion unit is used to input the prior wind speed and incident angle into the constructed Doppler centroid feature function model, and calculate the corresponding Doppler centroid feature prediction value of the receiver under different relative wind direction angles. The wind direction inversion unit is used to construct a joint cost function based on the relative azimuth angle deflection of the two receivers, the predicted value of the Doppler centroid feature, and the observed value of the Doppler centroid feature. The value of the relative wind direction angle is adjusted until the joint cost function reaches its minimum value, and the relative wind direction angle corresponding to the minimum value is output. The wind direction inversion unit is used to obtain the sea surface wind direction of the sea surface grid based on the output relative wind direction angle.

[0014] Thirdly, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method provided by the first aspect of this application or any implementation thereof.

[0015] Fourthly, this application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided by the first aspect of this application or any implementation thereof.

[0016] The beneficial effects of the technical solution provided in this application include at least the following: This application employs dual-satellite GNSS-R receivers to simultaneously observe the same sea surface grid, extracts Doppler centroid feature observations, and calculates predicted values ​​based on the Doppler centroid feature function model. It then constructs a joint cost function to optimize and obtain unambiguous sea surface wind direction. This overcomes the limitations of traditional methods that only focus on the high-energy region of time-delay-Doppler mapping data or use fixed thresholds to extract Doppler centroid features. By utilizing pixel-level centroid contribution analysis to lock the deep time-delay tail region of time-delay-Doppler mapping data and cross-domain mapping to the Doppler frequency domain, the application significantly amplifies the dynamic response range of wind direction features, improves noise robustness in low signal-to-noise ratio environments, and achieves high-fidelity extraction of weak wind direction features.

[0017] Meanwhile, the dual-satellite collaborative observation architecture, combined with the spatial azimuth deflection, constructs a joint cost function, effectively overcoming the 180-degree double-solution ambiguity caused by the forward scattering geometric symmetry under the single-satellite system. It achieves a unique global minimum optimization from a physical spatial geometry perspective, completely eliminating the multi-solution ambiguity of wind direction inversion. In addition, the Doppler centroid feature function model constructed based on measured data and auxiliary wind vector products retains strict physical prior constraints, avoiding the uninterpretability and overfitting risks of machine learning black box models, significantly reducing the computational power consumption of inversion, ensuring the rapid and stable output of wind direction products, and better meeting the real-time and near-real-time production needs of spaceborne GNSS-R marine meteorological products. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is one of the flowcharts illustrating a method for unambiguous sea surface wind direction retrieval based on spaceborne GNSS-R provided in this application embodiment; Figure 2 This is the second schematic diagram of the unambiguous inversion of sea surface wind direction by spaceborne GNSS-R provided in the embodiments of this application; Figure 3 This is one of the schematic diagrams of the Doppler centroid characteristics changing with wind direction provided in the embodiments of this application; Figure 4 This is the second schematic diagram of the curve showing the change of Doppler centroid characteristics with wind direction provided in the embodiments of this application; Figure 5 This is a schematic diagram of a sea surface wind direction unambiguous inversion device based on spaceborne GNSS-R provided in an embodiment of this application.

[0020] Figure 6 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or modules is not limited to the steps or modules listed, but may optionally include steps or modules not listed, or may optionally include other steps or modules inherent to such process, method, product, or apparatus.

[0023] It should be noted that the terms "first" and "second" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects. It is understood that "first" and "second" can be interchanged in a specific order or sequence where permitted. It should be understood that the objects distinguished by "first" and "second" can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in an order other than those described or illustrated herein.

[0024] The present application will now be described in detail with reference to specific embodiments.

[0025] Next, combine Figure 1 This paper introduces a method for unambiguous inversion of sea surface wind direction based on spaceborne GNSS-R, as provided in an embodiment of this application. For details, please refer to... Figure 1 , Figure 1 This diagram illustrates a flowchart of a method for unambiguous sea surface wind direction retrieval based on spaceborne GNSS-R, as provided in an embodiment of this application. Figure 1 As shown, the method includes the following steps: S101, determine two spaceborne GNSS-R receivers in the same sea surface grid within the target sea area for synchronous observation, and acquire GNSS-R observation data respectively; S102, obtain the Doppler centroid feature observation value of the corresponding receiver based on the time delay-Doppler mapping data; obtain the prior wind speed of the corresponding sea surface grid based on the wind field observation data within the same time period; S103: Input the prior wind speed and incident angle into the constructed Doppler centroid feature function model, and calculate the corresponding Doppler centroid feature prediction value of the receiver under different relative wind direction angles. S104: Construct a joint cost function based on the relative azimuth deflection of the two receivers, the predicted value of the Doppler centroid feature, and the observed value of the Doppler centroid feature. Adjust the value of the relative wind direction angle until the joint cost function reaches its minimum value, and output the relative wind direction angle corresponding to the minimum value. S105, the sea surface wind direction of the sea surface grid is obtained by processing the output relative wind direction angle.

[0026] In some embodiments, a schematic diagram illustrating the observation of the target sea area by paired GNSS-R satellites during the actual wind direction inversion process is shown below. Figure 2 As shown, refer to Figure 2 As illustrated, GNSS signals can be synchronously transmitted to the same sea surface grid by satellite-borne GNSS transmitter A and satellite-borne GNSS transmitter B, respectively. The GNSS signals can then be received by GNSS-R receivers R1 and R2, respectively, thus obtaining the corresponding GNSS-R observation data. This GNSS-R observation data includes time delay-Doppler mapping data (DDM) and incident angle... and the relative azimuth deflection between receivers .

[0027] Specifically, the GNSS signals from the spaceborne GNSS transmitter can reach the corresponding GNSS-R receiver via both the reflection path and the direct path.

[0028] like Figure 2 As shown, GNSS-R receiver R1 can receive GNSS signals from onboard GNSS transmitter A via reflection path A (reflected by the sea surface at the mirror point) and GNSS signals directly received via direct path A. Based on these two paths, correlation calculations can be performed to obtain the time-delay-Doppler mapping data for GNSS-R receiver R1. Similarly, the process for obtaining GNSS-R observation data from GNSS-R receiver R2 is the same as that for GNSS-R receiver R1, and the time-delay-Doppler mapping data for GNSS-R receiver R2 can also be calculated. This will not be elaborated further here.

[0029] The embodiments of this application can filter GNSS-R observation data of paired GNSS-R receivers through preset spatiotemporal constraints, such as the time difference between the two GNSS-R receivers being less than a preset time difference threshold (e.g., set to 10 minutes), and the spatial distance between the observed sea surface grids being less than a preset distance threshold (e.g., set to 9 km).

[0030] In some embodiments, considering that the wind-sensitive signal in the deep time-delay tail region is relatively weak and easily submerged by the receiver's thermal noise and speckle noise, if it is not suppressed, the background noise will severely deviate the integral result of the Doppler centroid feature. Therefore, it is necessary to process the time-delay-Doppler mapping data.

[0031] It should be noted that the deep delay tail region refers to the two-dimensional pixel interval on the delay axis that is located at a relatively long distance after the time delay corresponding to the specular reflection point of the time delay-Doppler mapping data, and where the signal energy gradually decreases with increasing distance; the specular reflection point of the time delay-Doppler mapping data refers to the pixel position where both the time delay axis and the Doppler axis are 0 in the time delay-Doppler mapping data; the pure noise delay interval is the signal-free echo region located several time delay units before the arrival time of the specular reflection point. For example, the first 20 time delay units on the DDM time delay axis can be selected as the pure noise delay interval.

[0032] Specifically, the steps in S102 to obtain the corresponding Doppler centroid feature observations based on time-delay-Doppler mapping data processing include: S1021, determine the pure noise time delay interval of the time delay-Doppler mapping data, and calculate the noise floor reference based on the statistical mean of the scattered power within the pure noise time delay interval.

[0033] S1022, Based on the noise floor reference, perform noise removal on the time delay-Doppler mapping data to obtain the denoised time delay-Doppler mapping data; Specifically, the noise baseline can be uniformly subtracted from each cell value in the matrix of the original time-delay-Doppler mapping data.

[0034] Optionally, to ensure the physical meaning of the signal, if the pixel value after subtraction is negative, the corresponding pixel value will be forcibly truncated to 0.

[0035] S1023, based on the denoised time-delay-Doppler mapping data, normalization processing is performed to map the relative power corresponding to the peak value to 1, thereby obtaining the normalized time-delay-Doppler mapping data.

[0036] Furthermore, to completely remove the integral accumulation effect of residual speckle noise, this application embodiment introduces a noise reduction threshold mask based on a dynamic noise reduction threshold, performing the following steps: S1024, the normalized time-delay-Doppler mapping data is processed by a preset noise reduction threshold mask, and the pixels in the normalized time-delay-Doppler mapping data whose signal power is less than the noise reduction threshold corresponding to the noise reduction threshold mask are assigned a value of 0, so as to obtain the masked time-delay-Doppler mapping data.

[0037] Specifically, the value of the dynamic noise reduction threshold can be selected according to the actual situation. For example, 10% to 20% of the peak power can be selected as the noise reduction threshold, preferably 15%. This application embodiment does not limit this.

[0038] S1025, Doppler centroid feature observations are extracted from the time-delay-Doppler mapping data after masking, using the following formula: ; in, This represents the extracted Doppler centroid feature observations. For the time delay of the time-Doppler mapping data, For the corresponding delay-Doppler mapping data, the Doppler frequency, This represents the time-delay-Doppler mapping data after masking.

[0039] In some embodiments, a Doppler centroid feature function model can be constructed using sample observation data collected within a historical time period. The construction process includes the following steps: S201: Acquire GNSS-R sample data for each time period obtained from observations of the target sea area by multiple spaceborne GNSS-R receivers, and obtain wind field sample data matching each GNSS-R sample data based on the wind field observation data within the corresponding time period. S202, based on the time delay-Doppler mapping data in the GNSS-R sample data, the corresponding Doppler centroid feature observation values ​​are obtained.

[0040] S203, based on the GNSS-R sample data, the sample azimuth angle and sample incident angle are extracted; based on the wind field sample data, the sample wind direction angle and sample wind speed are extracted; based on the sample wind direction angle and sample azimuth angle, the sample relative wind direction angle is calculated.

[0041] Specifically, GNSS-R sample data includes time delay-Doppler mapping data, sample azimuth angle and sample incident angle, and may also include the data acquisition time period, the mirror point position of the GNSS signal reflected in the sea area, the signal-to-noise ratio of the signal, quality control bits, etc., but this application embodiment does not limit these.

[0042] Specifically, wind field observation data can be ERA5 reanalysis data, CCMP wind field data, or ocean buoy measurement data, etc. The true wind direction angle and true wind speed within the corresponding time period can be easily extracted and used as sample wind direction angle and sample wind speed for constructing the Doppler centroid characteristic function model.

[0043] Specifically, by setting a strict spatiotemporal matching window (the time window is set to within 30 minutes and the spatial distance window is set to 25km), GNSS-R sample data and wind field sample data can be matched one by one to construct a high-quality sample set for subsequent construction of the Doppler centroid feature function model.

[0044] It should be noted that the process of obtaining the corresponding Doppler centroid feature observations based on time-delay-Doppler mapping data processing is described in S102 and will not be repeated here.

[0045] Following S203, quality control can be performed on the sample set to exclude some samples that do not meet the criteria, including: The sea surface grid position of the sample observation is determined based on the mirror point position corresponding to the GNSS-R sample data, excluding samples from land / sea ice areas and the edges of land / sea ice areas; Samples with negative values ​​can be removed from the sample set; It can also remove samples with an incident angle greater than a preset incident angle threshold, for example, setting the incident angle threshold to 50°; It can also remove samples where the quality control bit is not 0; It can also remove samples with a signal-to-noise ratio lower than a preset signal-to-noise ratio threshold.

[0046] The embodiments of this application do not limit the conditions for quality control.

[0047] S204 uses the Doppler centroid feature observations, relative wind direction angles, wind speeds, and incident angles of multiple GNSS-R sample data to fit the data and determine the relationship between the Doppler centroid feature observations and the relative wind direction angles. Sample wind speed and sample incident angle The mapping relationship between them is used to construct the Doppler centroid eigenfunction model. The formula is expressed as: .

[0048] It should be noted that, by constructing this Doppler centroid feature function model, the embodiments of this application accurately quantify the anisotropic (i.e., wind direction sensitivity) response law of the Doppler centroid feature observations as a function of relative wind direction after masking. This can be referred to... Figure 3 The graph shows the sensitivity relationship between Doppler centroid feature observations and wind direction at a fixed wind speed and different incident angles. Figure 4 The example shows the sensitivity curves of Doppler centroid feature observations to wind direction at a fixed incident angle and different sea surface wind speeds.

[0049] Combination Figure 3 and Figure 4 The embodiments of this application fully verify the wind direction sensitivity of the Doppler centroid feature observations after masking through physical simulation. Figure 3 The study demonstrates the variation of Doppler centroid characteristics with relative wind direction (0°~360°) under a fixed sea surface wind speed of 15 m / s. It can be seen that the centroid characteristics exhibit a highly consistent cosine-like anisotropic response at different incident angles ranging from 20° to 45°; however, the overall dynamic range of the centroid curve gradually narrows as the incident angle increases.

[0050] Figure 4 This study further demonstrates the dynamic evolution of Doppler centroid feature observations under different sea surface wind speeds (5 m / s to 19 m / s) with relative wind direction, under a fixed observation incident angle of 30°. It can be seen that as wind speed increases, sea surface roughness and asymmetry intensify, and the amplitude of the Doppler centroid curve (i.e., the dynamic range of wind direction features) is significantly amplified. Figure 3 and Figure 4 This fully demonstrates that there is a stable and significant quantitative mapping relationship between the Doppler centroid feature observations extracted in this application and wind speed, relative wind direction, and incident angle, providing a reliable physical basis for the construction of the Doppler centroid feature function model.

[0051] In some embodiments, if the total number of GNSS-R sample data is greater than a preset threshold, the signal quality parameters calculated based on the delay-Doppler mapping data of all GNSS-R sample data are greater than or equal to the signal-to-noise ratio threshold, and all GNSS-R sample data are uniformly distributed within the ranges of relative wind direction angle, wind speed, and incident angle, then the step of constructing the Doppler centroid feature function model further includes: S301, based on the distribution of all GNSS-R sample data within the range of relative wind direction angle, wind speed and incident angle, the grid is divided to determine a three-dimensional feature grid with relative wind direction angle, wind speed and incident angle as feature dimensions.

[0052] For example, the grid division step for wind speed is set to 1 meter, the grid division step for incident angle is set to 1 degree, and the grid division step for relative wind direction angle is set to 5 degrees (or 10 degrees). This application embodiment does not limit this.

[0053] S302, fill the corresponding three-dimensional feature grid with the Doppler centroid feature observation value corresponding to each GNSS-R sample data.

[0054] S303, perform probability density analysis on the Doppler centroid feature observations in each three-dimensional feature grid, remove outlier points with deviations greater than a preset deviation threshold, and calculate the mean of the remaining Doppler centroid feature observations as the Doppler centroid feature baseline value of the corresponding three-dimensional feature grid. S304 uses relative wind direction angle, wind speed, and incident angle as independent variables, and the Doppler centroid feature reference value of each three-dimensional feature grid as the dependent variable. The mapping relationship between the independent and dependent variables is determined by multivariate nonlinear regression (preferably using truncated Fourier series / harmonic function for fitting) or three-dimensional smooth spline interpolation algorithm, thus constructing the Doppler centroid feature function model.

[0055] It should be noted that the constructed Doppler centroid feature function model can be understood as applying a mapping relationship by inputting wind speed, relative wind direction, and incident angle: This yields the predicted values ​​output by the model, namely the predicted values ​​of the Doppler centroid features.

[0056] In some embodiments, if the actual amount of sample data obtained is small, i.e., the total number of GNSS-R sample data is less than or equal to a preset number threshold, the step of constructing the Doppler centroid feature function model further includes: S401, Configure the parameters of the spaceborne GNSS-R in the physical simulator, and perform simulation by adjusting the simulated wind direction angle and simulated wind speed through the physical simulator to obtain GNSS-R simulation data.

[0057] Specifically, the wind direction angle and simulated wind speed can be adjusted within the full wind direction range (0°~360°) and the preset wind speed range to obtain uniformly distributed simulation data.

[0058] S402, based on the theoretical time delay-Doppler mapping data processing of GNSS-R simulation data, the corresponding theoretical value of the Doppler centroid feature is obtained.

[0059] Specifically, the process of obtaining the corresponding theoretical values ​​of the Doppler centroid features based on the theoretical time delay-Doppler mapping data is the same as described in reference S102, and will not be repeated here.

[0060] S403, using the theoretical value of the Doppler centroid feature obtained from simulation as the dependent variable, and the relative wind direction angle, wind speed and incident angle obtained from simulation as independent variables, the mapping relationship between the dependent and independent variables is determined, and the initial Doppler centroid feature function model is constructed.

[0061] Understandably, the initial Doppler centroid characteristic function model is an initial model fitted based on simulation data obtained from a physical simulator. Further calibration and verification are then performed using existing GNSS-R sample data, including the following steps: S404. The GNSS-R sample data are divided into a calibration set and a validation set. The relative wind direction angle, wind speed and incident angle of each GNSS-R sample data in the calibration set are input into the initial Doppler centroid feature function model to obtain the Doppler centroid feature mapping value.

[0062] S405, adjust the model parameters of the initial Doppler centroid feature function model according to the difference between the Doppler centroid feature mapping value and the Doppler centroid feature observation value of each GNSS-R sample data in the calibration set, and obtain the calibrated Doppler centroid feature function model.

[0063] Specifically, the error residual can be calculated based on the difference between the Doppler centroid feature mapping value and the Doppler centroid feature observation value of each GNSS-R sample data in the calibration set. Then, the model parameters of the initial Doppler centroid feature function model can be adjusted according to the distribution law of the error residual. For example, a linear scaling factor or bias smoothing term can be introduced according to the distribution law of the error residual to obtain the calibrated Doppler centroid feature function model.

[0064] Furthermore, cross-validation evaluation is performed using a validation set, including: S406. Input the relative wind direction angle, wind speed and incident angle of each GNSS-R sample data in the validation set into the calibrated Doppler centroid feature function model to obtain the predicted value of the Doppler centroid feature. S407, Calculate the root mean square error based on the predicted and observed Doppler centroid features of each GNSS-R sample data in the validation set; S408, if the root mean square error is less than the preset error threshold, the calibrated Doppler centroid feature function model is verified and the calibrated Doppler centroid feature function model is output. S409, otherwise, adjust the simulation parameters of the physical simulator, and re-execute the steps of configuring the parameters of the spaceborne GNSS-R in the physical simulator and the subsequent steps until the calibrated Doppler centroid characteristic function model is verified.

[0065] In some embodiments, in S104, either of the two synchronously observed spaceborne GNSS-R receivers can be selected as the reference spaceborne GNSS-R receiver, and the relative azimuth deflection, Doppler centroid feature prediction, and Doppler centroid feature observation of the two spaceborne GNSS-R receivers can be input into the constructed joint cost function.

[0066] The formula for the constructed joint cost function is expressed as follows: ; In this context, subscript A represents the reference satellite GNSS-R receiver among the two satellite GNSS-R receivers used for synchronous observation, and subscript B represents the other satellite GNSS-R receiver. The relative wind direction angle to be solved is specifically the difference between the actual wind direction and the GNSS-R observation azimuth angle of the reference satellite-borne GNSS-R receiver. This indicates the relative azimuth deflection between two spaceborne GNSS-R receivers; This represents the Doppler centroid characteristic observations of the reference spaceborne GNSS-R receiver. This represents the Doppler centroid feature observations from another spaceborne GNSS-R receiver; This indicates the angle of incidence of the reference spaceborne GNSS-R receiver. This indicates the angle of incidence for another spaceborne GNSS-R receiver; Indicates the prior wind speed; This represents the constructed Doppler centroid eigenfunction model. Indicates prior wind speed Relative wind angle and the incident angle of the reference spaceborne GNSS-R receiver The predicted Doppler centroid feature value is obtained after inputting the Doppler centroid feature function model. This represents the predicted Doppler centroid feature value of another spaceborne GNSS-R receiver.

[0067] When solving the problem, the search space can be traversed within a range of 0 to 360 degrees. Find the joint cost function The solution that obtains the global minimum value The formula is expressed as: ; Understandably, by introducing geometric deflection... Two sets of cost function curves, generated from observation data from two simultaneously observed GNSS-R receivers, are superimposed here. The valley corresponding to the true wind direction is perfectly aligned and overlaps in both sets of curves, while the pseudo-valley, which was originally about 180 degrees different, is due to... Their existence caused them to be misaligned and then filled in.

[0068] Then, in S105, the final relative wind direction angle is obtained. By combining the actual observed azimuth angle from the reference satellite-borne GNSS-R receiver, it can be converted into a unique actual sea surface wind direction in the geographic coordinate system, thus completely eliminating the ambiguity of multiple solutions in the inversion.

[0069] The following are device embodiments of this application, which can be used to execute the method embodiments of this application. For details not disclosed in the device embodiments of this application, please refer to the method embodiments of this application.

[0070] Please see below. Figure 5 The diagram below illustrates a structural design of a fuzz-free sea surface wind direction inversion device based on a spaceborne GNSS-R, as provided in an exemplary embodiment of this application. The device includes: The data acquisition unit is used to identify two spaceborne GNSS-R receivers in the same sea surface grid within the target sea area for synchronous observation, and to acquire GNSS-R observation data respectively; the GNSS-R observation data includes time delay-Doppler mapping data, incident angle information, and relative azimuth angle deflection between the receivers; The data processing unit is used to obtain the Doppler centroid feature observation value of the corresponding receiver based on the time delay-Doppler mapping data; and to obtain the prior wind speed of the corresponding sea surface grid based on the wind field observation data within the same time period. The wind direction inversion unit is used to input the prior wind speed and incident angle into the constructed Doppler centroid feature function model, and calculate the corresponding Doppler centroid feature prediction value of the receiver under different relative wind direction angles. The wind direction inversion unit is used to construct a joint cost function based on the relative azimuth angle deflection of the two receivers, the predicted value of the Doppler centroid feature, and the observed value of the Doppler centroid feature. The value of the relative wind direction angle is adjusted until the joint cost function reaches its minimum value, and the relative wind direction angle corresponding to the minimum value is output. The wind direction inversion unit is used to obtain the sea surface wind direction of the sea surface grid based on the output relative wind direction angle.

[0071] It should be noted that the apparatus provided in the above embodiments, when executing a fuzzy-free sea surface wind direction inversion method based on spaceborne GNSS-R, is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. Furthermore, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and their implementation process is detailed in the method embodiments, which will not be repeated here.

[0072] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of any of the methods described above.

[0073] Please see Figure 6 This is a structural block diagram of an electronic device provided in an embodiment of this application.

[0074] like Figure 6 As shown, the electronic device includes a processor and a memory.

[0075] In this embodiment, the processor is the control center of the computer system, and can be a processor of a physical machine or a processor of a virtual machine. The processor may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor can be implemented using at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array).

[0076] A processor can also include a main processor and a coprocessor. The main processor is used to process data in the wake-up state and is also called the CPU (Central Processing Unit). The coprocessor is a low-power processor used to process data in the standby state.

[0077] The memory may include one or more computer-readable storage media, which may be non-transitory. The memory may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments of this application, the non-transitory computer-readable storage media in the memory are used to store at least one instruction, which is executed by a processor to implement the methods in the embodiments of this application.

[0078] In some embodiments, the electronic device further includes a peripheral device interface and at least one peripheral device. The processor, memory, and peripheral device interface are connected via a bus or signal line. Each peripheral device is connected to the peripheral device interface via a bus, signal line, or circuit board. Specifically, the peripheral device includes: a display screen, a camera, and audio circuitry. The peripheral device interface can be used to connect at least one I / O (Input / Output) related peripheral device to the processor and memory.

[0079] In some embodiments of this application, the processor, memory, and peripheral device interfaces are integrated on the same chip or circuit board; in other embodiments of this application, any one or two of the processor, memory, and peripheral device interfaces can be implemented on separate chips or circuit boards. This application does not specifically limit the implementation in this regard.

[0080] The electronic device structural block diagrams shown in the embodiments of this application do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0081] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the methods in any of the foregoing embodiments. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0082] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of software products. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for unambiguous inversion of sea surface wind direction based on spaceborne GNSS-R, characterized in that, include: Two spaceborne GNSS-R receivers within the same sea surface grid in the target sea area are identified for synchronous observation, and GNSS-R observation data are acquired separately. The GNSS-R observation data includes time delay-Doppler mapping data, incident angle, and relative azimuth deflection between receivers; Based on the time-delay-Doppler mapping data, obtain the Doppler centroid feature observation values ​​of the corresponding receiver; Prior wind speeds for the corresponding sea surface grids are obtained based on wind field observation data within the same time period. Input the prior wind speed and incident angle into the constructed Doppler centroid feature function model, and calculate the corresponding Doppler centroid feature prediction values ​​of the receiver under different relative wind angles. A joint cost function is constructed based on the relative azimuth deflection of the two receivers, the predicted value of the Doppler centroid feature, and the observed value of the Doppler centroid feature. The value of the relative wind direction angle is adjusted until the joint cost function reaches its minimum value, and the relative wind direction angle corresponding to the minimum value is output. The sea surface wind direction of the sea surface grid is obtained by processing the output relative wind direction angle; The formula for the joint cost function is expressed as follows: ； In this context, subscript A represents the reference receiver among the two spaceborne GNSS-R receivers used for synchronous observation, and subscript B represents the other receiver. The relative wind direction angle to be solved; This indicates the relative azimuth deflection. This represents the Doppler centroid characteristic observation value of the reference receiver. This represents the Doppler centroid feature observation value of another receiver; Indicates the angle of incidence of the reference receiver. Indicates the angle of incidence of the other receiver; Indicates the prior wind speed; This represents the constructed Doppler centroid eigenfunction model. This represents the predicted Doppler centroid feature value corresponding to the reference receiver. This represents the predicted Doppler centroid feature value corresponding to the other receiver.

2. The unambiguous inversion method for sea surface wind direction based on spaceborne GNSS-R according to claim 1, characterized in that, The construction process of the Doppler centroid eigenfunction model includes the following steps: Acquire GNSS-R sample data for various time periods obtained from observations of the target sea area by multiple spaceborne GNSS-R receivers, and obtain wind field sample data matching each GNSS-R sample data based on wind field observation data within the corresponding time period; Based on the time-delay-Doppler mapping data in the GNSS-R sample data, the corresponding Doppler centroid feature observation values ​​are obtained; The sample azimuth and incident angle are extracted from the GNSS-R sample data, the sample wind direction and wind speed are extracted from the wind field sample data, and the sample relative wind direction angle is calculated based on the sample wind direction and sample azimuth. By fitting the Doppler centroid feature observations, relative wind direction angle, wind speed, and incident angle of multiple GNSS-R sample data, the mapping relationship between the Doppler centroid feature observations and the relative wind direction angle, wind speed, and incident angle is determined, and a Doppler centroid feature function model is constructed.

3. The unambiguous inversion method for sea surface wind direction based on spaceborne GNSS-R according to claim 1 or 2, characterized in that, The steps for obtaining the corresponding Doppler centroid feature observations based on time-delay-Doppler mapping data processing include: The pure noise time delay interval of the time delay-Doppler mapping data is determined, and the noise floor reference is calculated based on the statistical mean of the scattered power within the pure noise time delay interval. Based on the noise floor benchmark, the time delay-Doppler mapping data is subjected to noise removal to obtain the denoised time delay-Doppler mapping data. The denoised time-delay-Doppler mapping data is normalized to obtain normalized time-delay-Doppler mapping data. The normalized time-delay-Doppler mapping data is processed by a preset noise reduction threshold mask. Pixels in the normalized time-delay-Doppler mapping data whose signal power is less than the noise reduction threshold corresponding to the noise reduction threshold mask are assigned a value of 0, thus obtaining the masked time-delay-Doppler mapping data. Doppler centroid feature observations were extracted from the time-delay-Doppler mapping data after masking, using the following formula: ； in, This represents the extracted Doppler centroid feature observations. For the time delay of the time-Doppler mapping data, For the corresponding delay-Doppler mapping data, the Doppler frequency, This represents the time-delay-Doppler mapping data after masking.

4. The unambiguous inversion method for sea surface wind direction based on spaceborne GNSS-R according to claim 2, characterized in that, If the total number of GNSS-R sample data exceeds a preset threshold, the signal quality parameters calculated based on the delay-Doppler mapping data of all GNSS-R sample data are greater than or equal to the signal-to-noise ratio threshold, and all GNSS-R sample data are uniformly distributed within the ranges of relative wind direction angle, wind speed, and incident angle, then the steps for constructing the Doppler centroid feature function model further include: The grid is divided based on the distribution of all GNSS-R sample data within the range of relative wind direction angle, wind speed and incident angle, and a three-dimensional feature grid with relative wind direction angle, wind speed and incident angle as feature dimensions is obtained. Fill the corresponding three-dimensional feature grid with the Doppler centroid feature observation value corresponding to each GNSS-R sample data; Probability density analysis is performed on the Doppler centroid feature observations in each three-dimensional feature grid. Abnormal outliers with deviations greater than a preset deviation threshold are removed. The mean of the remaining Doppler centroid feature observations is calculated and used as the Doppler centroid feature baseline value of the corresponding three-dimensional feature grid. Using relative wind direction angle, wind speed, and incident angle as independent variables, and the Doppler centroid feature benchmark value of each three-dimensional feature grid as the dependent variable, the mapping relationship between the independent and dependent variables is determined by multivariate nonlinear regression, thus constructing the Doppler centroid feature function model.

5. The unambiguous inversion method for sea surface wind direction based on spaceborne GNSS-R according to claim 2, characterized in that, If the total number of GNSS-R sample data is less than or equal to a preset threshold, the steps for constructing the Doppler centroid feature function model also include: Configure the parameters of the spaceborne GNSS-R in the physical simulator, and perform simulation by adjusting the simulated wind direction angle and simulated wind speed through the physical simulator to obtain GNSS-R simulation data; The theoretical values ​​of the corresponding Doppler centroid characteristics are obtained by processing the theoretical time delay-Doppler mapping data of GNSS-R simulation data. Using the theoretical values ​​of the Doppler centroid features obtained from simulation as the dependent variable, and the relative wind direction angle, wind speed, and incident angle obtained from simulation as the independent variables, the mapping relationship between the dependent and independent variables is determined, and the initial Doppler centroid feature function model is constructed. The GNSS-R sample data are divided into a calibration set and a validation set. The relative wind direction angle, wind speed and incident angle of each GNSS-R sample data in the calibration set are input into the initial Doppler centroid feature function model to obtain the Doppler centroid feature mapping value. The model parameters of the initial Doppler centroid feature function model are adjusted based on the difference between the Doppler centroid feature mapping value and the Doppler centroid feature observation value of each GNSS-R sample data in the calibration set, so as to obtain the calibrated Doppler centroid feature function model.

6. The unambiguous inversion method for sea surface wind direction based on spaceborne GNSS-R according to claim 5, characterized in that, After obtaining the calibrated Doppler centroid eigenfunction model, the method further includes: Input the relative wind direction angle, wind speed and incident angle of each GNSS-R sample data in the validation set into the calibrated Doppler centroid feature function model to obtain the predicted value of the Doppler centroid feature. The root mean square error is calculated based on the predicted and observed Doppler centroid features of each GNSS-R sample data in the validation set. If the root mean square error is less than the preset error threshold, the calibrated Doppler centroid feature function model is verified and the calibrated Doppler centroid feature function model is output. Otherwise, adjust the simulation parameters of the physics simulator and re-execute the steps for configuring the parameters of the spaceborne GNSS-R in the physics simulator until the calibrated Doppler centroid characteristic function model is verified.

7. An apparatus for unambiguous inversion of sea surface wind direction based on the spaceborne GNSS-R method according to any one of claims 1-6, characterized in that, The device includes: The data acquisition unit is used to identify two spaceborne GNSS-R receivers in the same sea surface grid within the target sea area for synchronous observation, and to acquire GNSS-R observation data respectively; the GNSS-R observation data includes time delay-Doppler mapping data, incident angle information, and relative azimuth angle deflection between the receivers; The data processing unit is used to obtain the Doppler centroid feature observation value of the corresponding receiver based on the time delay-Doppler mapping data; and to obtain the prior wind speed of the corresponding sea surface grid based on the wind field observation data within the same time period. The wind direction inversion unit takes the prior wind speed and incident angle as input into the constructed Doppler centroid feature function model and calculates the corresponding Doppler centroid feature prediction values ​​of the receiver under different relative wind direction angles. The wind direction inversion unit is used to construct a joint cost function based on the relative azimuth angle deflection of the two receivers, the predicted value of the Doppler centroid feature, and the observed value of the Doppler centroid feature. The value of the relative wind direction angle is adjusted until the joint cost function reaches its minimum value, and the relative wind direction angle corresponding to the minimum value is output. The wind direction inversion unit is used to obtain the sea surface wind direction of the sea surface grid based on the output relative wind direction angle.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 6.

Images