A global average sea clutter near real-time estimation method, system, device and storage medium

Abstract

The application discloses a global average sea clutter near real-time estimation method, system, device and storage medium, including the following steps: obtaining multi-source satellite-borne radar historical backscattering coefficient data, and performing time and space matching and binning on the ERA5 wind field; an average sea clutter estimation model is established according to a geophysical model function fitting; the satellite-borne microwave scatterometer wind field is corrected in deviation and fused with a numerical weather prediction wind field, so that a high-coverage, continuous near real-time global sea surface wind field is obtained; the relative azimuth is calculated in combination with radar observation parameters and fused wind direction, and global average sea clutter data is obtained by using the model; the application improves the precision and application range of sea clutter estimation under the condition of medium and small incident angles, provides a high-quality input wind field through a multi-source wind field fusion technology, realizes rapid, stable and near real-time estimation of global average sea clutter, has high timeliness and business feasibility, and can meet the needs of near real-time sea clutter monitoring and ocean remote sensing application.

Description

Technical Field

[0001] This invention relates to the field of marine engineering technology, specifically to a near real-time estimation method, system, device, and storage medium for global mean sea clutter. Background Technology

[0002] Sea clutter is the backscattered signal generated on the sea surface under radar electromagnetic wave illumination. Its intensity is affected by various factors such as sea state, wind speed, incident angle, and polarization. Accurate estimation of sea clutter levels is crucial for target detection and identification at sea. By modeling the statistical characteristics of sea clutter, it is possible to effectively distinguish target echoes from background clutter, improving the reliability of detecting small targets at sea (such as ships and floating objects). Simultaneously, sea clutter estimation results can also be used for marine environmental monitoring, radar system design, and signal processing algorithm optimization, providing support for maritime safety monitoring, disaster early warning, and climate research. Therefore, achieving high-precision, global, and real-time sea clutter estimation has significant scientific value and application prospects for enhancing marine observation capabilities and supporting the construction of a maritime power.

[0003] Currently, existing sea clutter estimation models or methods are mainly developed based on radar observation data at large incident angles, and are often not applicable to sea clutter estimation under radar observation conditions at other incident angles. In addition, existing sea clutter estimation methods rely on satellite along-orbit wind field products or reanalysis wind field products as input, and the timeliness and spatial coverage of sea clutter estimation cannot be satisfied simultaneously. Summary of the Invention

[0004] Purpose of the invention: The purpose of this invention is to provide a near real-time estimation method, system, device and storage medium for global average sea clutter, which solves the problem that sea clutter estimation is often not applicable to radar observations with other incident angles, and that the timeliness and spatial coverage of sea clutter estimation cannot be satisfied at the same time.

[0005] Technical solution: The present invention provides a near-real-time estimation method for global mean sea clutter, comprising the following steps:

[0006] S1. Acquire backscattering coefficient data of multiple historical satellite-borne radars under different incident angles, polarization modes and observation azimuth angles. After quality control, perform spatiotemporal matching of ERA5 wind field data with radar observation data and parameters to construct a dataset.

[0007] S2. Calculate the relative wind direction data in the matching dataset based on the radar observation azimuth and background wind direction, and divide the matching dataset into data bins according to the polarization method and preset wind speed and incident angle interval.

[0008] S3. For each group of data after binning, fit the geophysical model function expression to construct a mean sea clutter estimation model.

[0009] S4. Acquire historical wind field data from multiple satellite-borne microwave scatterometers and perform grid conversion. Then, perform spatiotemporal matching with the ERA5 wind field and buoy wind field to calculate wind speed deviation under different loads and wind speed ranges, and establish a wind speed deviation correction lookup table.

[0010] S5. Obtain the wind field data of the satellite microwave scatterometer and the wind field data of numerical weather forecast, perform grid conversion on the satellite source wind field, use the wind speed deviation correction lookup table to correct the deviation of the satellite microwave scatterometer wind field, and realize the fusion of multi-source satellite sea surface wind fields through the two-dimensional variational method to obtain near real-time global sea surface wind field products.

[0011] S6. Calculate the relative azimuth angle based on radar observation parameters and fused wind field direction, and obtain global average sea clutter data using the mean sea clutter estimation model.

[0012] Furthermore, in S2, the data binning is as follows: First, the data is separated according to HH polarization and VV polarization, and then binned according to an incident angle interval of 1° and a wind speed interval of 0.2m / s.

[0013] Further in S3, the geophysical model function expression is:

[0014] σ0(θ, ν, φ) = A0(ν, θ)[1+ A1(ν, θ)cosφ+ A2(ν, θ)cos(2φ)]

[0015] Where σ0 is the radar measurement backscattering coefficient, θ is the radar observation incident angle, ν is the background wind speed, φ is the observation relative azimuth angle, the fitting method is the nonlinear least squares method, and A0, A1 and A2 are the coefficients obtained in each fitting. Finally, the values ​​of the three coefficients of the mean sea clutter estimation model under each polarization mode based on the selected incident angle and wind speed conditions can be obtained.

[0016] Furthermore, in S4 and S5, the grid conversion method is as follows: the star source level 2 wind field data organized by the swath grid is converted into regular latitude and longitude grid data, with a grid longitude and latitude interval of 0.125°, a longitude range of 0.0625° to 359.9375°, and a latitude range of -89.9375° to 89.9375°. After conversion, the Gao's rendering method is used to calculate the grid point wind field.

[0017] In further step S4, the wind speed deviation correction lookup table is established as follows: the average wind speed deviation between the satellite remote sensing data and the ERA5 wind field is calculated respectively, and the average value of the two is used as the deviation correction factor to form a lookup table.

[0018] Further in S5, the two-dimensional variational wind field fusion method is as follows: a target function is constructed using satellite-observed wind field data as the observation term and numerical weather prediction wind field as the background term; the analyzed wind field is obtained by minimizing the target function, where the observation term and background term respectively consider the observation error covariance and background error covariance, and the weight of the observation term is optimized by adjusting the factors.

[0019] The present invention provides a near real-time global mean sea clutter estimation system, comprising:

[0020] The data acquisition and matching module is used to implement data acquisition, quality control, spatiotemporal matching, and grid conversion functions.

[0021] The model building module is used to implement the functions of building data binning and mean sea clutter estimation models;

[0022] The wind field fusion module is used to realize wind speed deviation correction, multi-source wind field fusion, and near real-time global sea surface wind field product generation functions;

[0023] The sea clutter estimation module is used for calculating global average sea clutter data.

[0024] An electronic device according to the present invention includes a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the steps of any of the methods described herein.

[0025] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the methods described herein.

[0026] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: (1) Based on a large amount of historical backscattering coefficient data of spaceborne radar and sea surface wind field data, and combined with geophysical model function expressions, the present invention establishes a stable and reliable mean sea clutter estimation model, which effectively solves the problem of insufficient applicability of the existing model under medium and small incident angle conditions, and significantly improves the accuracy and applicability of mean sea clutter estimation; The present invention can comprehensively utilize the sea surface wind field data retrieved by spaceborne radar and the wind field data of numerical weather prediction to achieve the fusion of multi-source wind field information, thereby obtaining global sea surface wind field data with continuous and seamless spatial coverage, providing high-quality input data support for mean sea clutter estimation on a global scale; The present invention can quickly generate high spatial resolution sea surface wind field fusion products by introducing the gridded conversion technology of satellite source data and the two-dimensional variational wind field fusion algorithm, thereby realizing the rapid estimation of global mean sea clutter. This method has high timeliness and operational feasibility, and can meet the needs of near real-time sea clutter monitoring and marine remote sensing applications. Attached Figure Description

[0027] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0028] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0029] Example 1

[0030] like Figure 1 As shown, this embodiment of the invention provides a near-real-time estimation method for global average sea clutter, specifically, as follows: Figure 1 As shown, the specific steps include the following:

[0031] S1. Obtain the backscattering coefficients of historical observations from the Sino-French Oceanographic Satellite scatterometer, and simultaneously obtain data information under the corresponding incident angle, polarization mode and observation azimuth. Perform quality control on the observation data according to the data quality label. Then, perform spatiotemporal matching between ERA5 wind field data and radar observation data to establish a dataset. The time distance threshold is 0.5 hours and the spatial distance threshold is 25 kilometers.

[0032] S2. Calculate and obtain the relative wind direction data in the matching dataset based on the radar observation azimuth and background wind direction, and divide the matching dataset into data bins according to the radar polarization method and a certain wind speed and incident angle interval.

[0033] S3. Fit the data to each binned dataset according to the geophysical model function expression to construct a mean sea clutter estimation model;

[0034] S4. Obtain historical wind field data from microwave scatterometers of Haiyang-2B and Haiyang-2C satellites and perform grid conversion. Perform spatiotemporal matching with ERA5 wind field and buoy wind field. The time matching window is 0.5 hours and the spatial matching window is 25 kilometers. Calculate wind speed deviation under different loads and wind speed segments and create a wind speed deviation correction lookup table.

[0035] S5. Obtain wind field data from the scatterometers of Haiyang-2B and Haiyang-2C satellites on February 1, 2025, as well as wind field data from numerical weather prediction provided by the China Meteorological Administration. Perform grid conversion on the wind field products from the satellite scatterometers. Use the wind speed deviation lookup table obtained in S3 to correct the deviation of the wind field from the satellite microwave scatterometer. Achieve fusion of the multi-source satellite sea surface wind fields through a two-dimensional variational method to obtain the global sea surface fused wind field products at 0:00 and 12:00 on February 1, 2025.

[0036] S6. The parameters for radar observation of the sea surface are determined as follows: Ku band, HH polarization, 40° incident angle and 90° observation azimuth. The relative azimuth is calculated based on the wind direction of the fused wind field. Using the average sea clutter estimation model obtained in S2 and the global fused sea surface wind field product obtained in S5, the global average sea clutter values ​​and spatial distribution at 0:00 and 12:00 on February 1, 2025, with Ku band HH polarization, 40° incident angle and 90° observation azimuth are calculated.

[0037] In this embodiment, the data binning of the matching dataset according to radar polarization and certain wind speed and incident angle intervals in S2 is specifically as follows: First, the data is separated according to HH polarization and VV polarization, and then the data is binned according to an incident angle of 1° and a wind speed interval of 0.2 m / s.

[0038] In this embodiment, the specific formula for the geophysical model function expression in S3 is as follows:

[0039] σ 0 (θ, ν, φ) = A0(ν, θ)[1+ A1(ν, θ)cosφ+ A2(ν, θ)cos(2φ)]

[0040] Where, σ 0 The backscattering coefficient of the Sino-French oceanographic satellite scatterometer was measured, where θ is the observation incident angle, ν is the background wind speed, φ is the observation relative azimuth angle, and the fitting method is the nonlinear least squares method. 0、 A1 and A2 are the coefficients obtained from each fitting. Finally, the values ​​of the three coefficients of the Ku-band mean sea clutter estimation model under HH polarization and VV polarization modes are obtained according to the selected incident angle and wind speed conditions.

[0041] In this embodiment, the satellite sea surface wind field data grid conversion method in S4 and S5 is as follows: the satellite source level 2 wind field (velocity) data organized by the swath grid is converted into high-level wind field data organized by a regular latitude and longitude grid. A regular latitude and longitude grid means that the longitude and latitude intervals between adjacent grid centers are both 0.125°; the longitude coordinate scale starts at 0.0625° and ends at 359.9375°, totaling 2880 points; the latitude coordinate scale starts at -89.9375° and ends at 89.9375°, totaling 1440 points. After the grid conversion, every two adjacent interpolation points and their adjacent measurement points form a triangle, and the wind field at the regular grid point locations is calculated using the Gao's rendering method.

[0042] In this embodiment, the method for obtaining the wind speed deviation lookup table in S4 is as follows: First, the wind speed deviation for different data is calculated. The calculation method for the wind speed deviation is as follows:

[0043]

[0044]

[0045] Where w represents the wind speed difference between satellite remote sensing data and ERA5, and i and j represent different wind speed segments and satellite sea surface wind dataset numbers, respectively. △ represents the wind speed difference between different data sources, and N represents the number of data points. The superscript S or E indicates that the bias is estimated as a function of satellite remote sensing data or ERA5 sea surface wind speed. Subsequently, the average of the above two equations is taken as the bias correction factor to form a bias correction lookup table.

[0046] In this embodiment, the two-dimensional variational wind field fusion method in S5 specifically involves: given a set of satellite observation data (x represents a vector, k represents the kth data source) and background wind field vector x b Then, the variational analysis of the wind field vector x is obtained by minimizing the following objective function:

[0047]

[0048] J o and J b These represent the observed term and the background wind field term, respectively. For ease of calculation, Δx = xx is usually used. b Replace the state vector x. Thus, the observation term in the above equation is written as:

[0049]

[0050] Where (i,j) represents the index of the grid point in the two-dimensional variational analysis space, N1 and N2 represent the total number of grid points in the two-dimensional space, and M represents the total number of grid points in the two-dimensional space. ij It represents the number of satellite-observed wind fields at grid point (i,j), t ij and l ij This refers to the component of the wind field analyzed at grid point (i,j), while and This is the component of the wind field observed by satellite at that location. and λ represents the error in the observed wind field, and is an empirical adjustment factor used to optimize the observation terms of the objective function. k表示 Satellite source data weighting factor; if the contribution of each type of satellite data is equal, then p k =1 / M ij And the background wind field item is written as follows:

[0051]

[0052] in The background wind field error covariance matrix is ​​generally expressed as the product of the wind field variance and the error correlation function through mathematical transformations.

[0053]

[0054] Here, B and P are both diagonal matrices, representing the variance and error correlation function of the background wind field, respectively.

Claims

1. A near-real-time estimation method for global mean sea clutter, characterized in that, Includes the following steps: S1. Acquire backscattering coefficient data of multiple historical satellite-borne radars under different incident angles, polarization modes and observation azimuth angles. After quality control, perform spatiotemporal matching of ERA5 wind field data with radar observation data and parameters to construct a dataset. S2. Calculate the relative wind direction data in the matching dataset based on the radar observation azimuth and background wind direction, and bin the matching dataset according to the polarization method and preset wind speed and incident angle intervals; the data binning is as follows: first separate the data according to HH polarization and VV polarization, and then bin it according to 1° incident angle interval and 0.2m / s wind speed interval. S3. For each group of data after binning, fit the data according to the geophysical model function expression to construct a mean sea clutter estimation model; the geophysical model function expression is: s 0 (θ, ν, φ) = A0(ν, θ)[1+ A1(ν, θ)cosφ+ A2(ν, θ)cos(2φ)] Where, σ 0 For radar backscattering coefficient measurement, θ is the radar observation incident angle, ν is the background wind speed, φ is the observation relative azimuth angle, and the fitting method is nonlinear least squares method, A 0、 A1 and A2 are the coefficients obtained from each fitting, and finally the values ​​of the three coefficients of the mean sea clutter estimation model under each polarization mode according to the selected incident angle and wind speed conditions are obtained; S4. Acquire historical wind field data from multiple satellite-borne microwave scatterometers and perform grid conversion. Perform spatiotemporal matching with the ERA5 wind field and buoy wind field, calculate wind speed deviation under different loads and wind speed segments, and establish a wind speed deviation correction lookup table. The specific steps for establishing the wind speed deviation correction lookup table are as follows: Calculate the average wind speed deviation between the satellite remote sensing data and the ERA5 wind field, and use the average of the two as the deviation correction factor to form a lookup table. S5. Obtain the wind field data of the satellite microwave scatterometer and the wind field data of numerical weather forecast, perform grid conversion on the satellite source wind field, use the wind speed deviation correction lookup table to correct the deviation of the satellite microwave scatterometer wind field, and realize the fusion of multi-source satellite sea surface wind fields through the two-dimensional variational method to obtain near real-time global sea surface wind field products. S6. Calculate the relative azimuth angle based on radar observation parameters and fused wind field direction, and obtain global average sea clutter data using the mean sea clutter estimation model.

2. The near-real-time estimation method for global mean sea clutter according to claim 1, characterized in that, In S4 and S5, the grid conversion method is as follows: the star source level 2 wind field data organized by the swath grid is converted into regular latitude and longitude grid data. The grid longitude interval and latitude interval are both 0.125°, the longitude range is 0.0625° to 359.9375°, and the latitude range is -89.9375° to 89.9375°. After conversion, the Gao's rendering method is used to calculate the grid point wind field.

3. The near-real-time estimation method for global mean sea clutter according to claim 1, characterized in that, In S5, the two-dimensional variational wind field fusion method is as follows: a target function is constructed using satellite-observed wind field data as the observation term and numerical weather prediction wind field as the background term; the analyzed wind field is obtained by minimizing the target function, where the observation term and background term respectively consider the observation error covariance and background error covariance, and the weight of the observation term is optimized by adjusting the factors.

4. A near real-time global mean sea clutter estimation system, characterized in that, The method described in any one of claims 1-3 is used to implement the method, comprising: The data acquisition and matching module is used to implement data acquisition, quality control, spatiotemporal matching, and grid conversion functions. The model building module is used to implement the functions of building data binning and mean sea clutter estimation models; The wind field fusion module is used to realize wind speed deviation correction, multi-source wind field fusion, and near real-time global sea surface wind field product generation functions; The sea clutter estimation module is used for calculating global average sea clutter data.

5. An electronic device comprising a processor and a memory, characterized in that, The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the method according to any one of claims 1-3.

6. A 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 according to any one of claims 1-3.

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