A method for quickly generating multi-attribute composite ground electromagnetic characteristics

Abstract

The application provides a multi-attribute composite ground electromagnetic characteristic fast generation method, which relates to the technical field of electromagnetic characteristic generation and comprises the following steps: presetting a radar parameter interval and a polarization type, obtaining a maximum feasible step length by using a dichotomy iteration method and generating a working condition parameter combination, calculating a single facet scattering field and constructing a multi-attribute composite ground electromagnetic characteristic database, completing grid processing and constructing a digital grid model based on a DEM and a ground attribute map, obtaining the facet scattering field by table lookup after coordinate transformation of actual working condition parameters, obtaining a total scattering field by coherent superposition, and finally generating RCS, one-dimensional range images and two-dimensional SAR images. Through the precalculation database construction, high-precision ground modeling, coordinate transformation and fast table lookup mode, the application realizes efficient solution of complex ground electromagnetic scattering, greatly reduces real-time calculation amount, improves simulation speed and calculation precision, and meets the engineering application requirements of radar detection, landform exploration and the like.

Description

Technical Field

[0001] This invention relates to the field of electromagnetic property generation technology, and more specifically, to a method for rapidly generating multi-attribute composite surface electromagnetic properties. Background Technology

[0002] With the widespread application of radar technology in fields such as geomorphological exploration and national defense security, the actual scenarios are mostly complex and multi-attribute composite surfaces such as undulating mountains and vegetation cover. Traditional simplified modeling based on free space and uniform background cannot adapt to the real environment, and the computational complexity of electromagnetic scattering is greatly increased, making it difficult to meet the engineering requirements of high precision and rapid simulation. The problem of limited accuracy of radar target detection and parameter inversion under complex backgrounds is becoming increasingly prominent.

[0003] Traditional radar electromagnetic scattering calculation methods are only applicable to uniform surfaces and simple scenarios. In complex environments with multiple attributes and undulating terrain, they suffer from drawbacks such as cumbersome modeling processes, large real-time computational load, and slow solution speed. At the same time, the reliance on simplification assumptions leads to low accuracy in scattering field calculations. It is impossible to balance efficiency and accuracy, and it is difficult to quickly generate RCS, one-dimensional range profiles, and two-dimensional SAR images, thus restricting radar target recognition and environmental detection capabilities.

[0004] Therefore, it is necessary to design a rapid method for generating multi-attribute composite surface electromagnetic properties to solve the problems of complex modeling, low computational efficiency, and insufficient accuracy of electromagnetic scattering of complex and undulating multi-attribute composite surfaces in existing technologies. Summary of the Invention

[0005] In view of this, the present invention proposes a method for rapidly generating multi-attribute composite surface electromagnetic properties, aiming to solve the problems of the prior art.

[0006] In one aspect, this invention proposes a method for rapidly generating multi-attribute composite surface electromagnetic properties, comprising: A set of non-polarization parameter ranges and polarization types for radar electromagnetic waves is preset, and the maximum feasible step size for each non-polarization parameter range is obtained based on the binary iterative method; independent combinations of operating parameters are obtained based on each maximum feasible step size. A set of predefined land surface types is established, and numerical calculations of the scattering field of a single surface element are performed on each land surface type in the set of land surface types based on the combination of operating parameters. A multi-attribute composite land surface electromagnetic feature database is constructed based on the land surface types, the combination of operating parameters, and the scattering field of the single surface element. Obtain a DEM digital elevation image and a composite surface attribute map of the entire area where the target is located; perform grid alignment and cropping on the DEM digital elevation image and the composite surface attribute map; perform interpolation and densification on the DEM digital elevation image; obtain a multi-attribute digital grid model of the surface based on the segmentation and grid partitioning of the DEM digital elevation image and the composite surface attribute map; The actual operating parameters of the radar are obtained, and the actual operating parameters are transformed into a local coordinate system based on the coordinate transformation matrix to obtain the local operating parameters of the current surface element; the scattering field of the single surface element corresponding to the current surface element is obtained based on the local operating parameters; the scattering field of the single surface element of all surface elements is obtained by traversing all surface elements. The imaging range is determined based on the radar illumination area, and the imaging range is divided into several imaging resolution units based on the radar resolution; the total scattering field of each imaging resolution unit is obtained, and the RCS, one-dimensional range image and two-dimensional SAR image are obtained based on each total scattering field.

[0007] Furthermore, when obtaining independent combinations of operating parameters based on each of the maximum feasible step sizes, the process includes: Based on the maximum feasible step size, each of the non-polarization parameter intervals is discretely sampled, and a set of non-polarization parameters corresponding to each of the non-polarization parameter intervals is constructed based on the collected non-polarization parameters. By combining the non-polarization parameters in each set of non-polarization parameters with the polarization types in each set of polarization types, several sets of independent operating condition parameter combinations are obtained.

[0008] Furthermore, when constructing a multi-attribute composite surface electromagnetic feature database based on the surface type, the combination of operating parameters, and the scattering field of a single surface element, the following steps are included: The multi-attribute composite surface electromagnetic feature database is divided into a corresponding number of electromagnetic feature sub-databases based on the number of surface types, and the electromagnetic feature sub-databases are named based on the surface types. For each set of operating condition parameter combinations, a scattering field data file is created in each of the electromagnetic feature sub-libraries; The scattered field data file is named based on the combination of operating parameters, and the scattered field of a single surface element is stored in the corresponding scattered field data file based on the surface type and the combination of operating parameters. Using the surface type as the primary index and the combination of operating parameters as the secondary index, a joint index marker is established for the scattering field of each individual surface element in the multi-attribute composite surface electromagnetic feature database based on the primary index and the secondary index.

[0009] Furthermore, when performing grid alignment and cropping on the DEM digital elevation image and the composite surface attribute division map, the following steps are included: The DEM digital elevation image includes pixel unit elevation values ​​and geographic reference information; the latitude and longitude coordinates of the pixel units of the DEM digital elevation image are obtained based on the geographic reference information, and the land surface type is divided into the whole area based on the RGB colors in the composite land surface attribute division map; Obtain the latitude and longitude coordinates of the corner points of the composite surface attribute division map, and perform grid alignment between the composite surface attribute division map and the DEM digital elevation image based on the latitude and longitude coordinates of the corner points and the latitude and longitude coordinates of the pixel units; The portion of the DEM digital elevation image that extends beyond the composite surface attribute delineation map is cropped.

[0010] Furthermore, when obtaining a multi-attribute digital grid model of the surface based on segmentation and gridding of the DEM digital elevation image and the composite surface attribute partitioning map, the process includes: The DEM digital elevation image and the composite surface attribute map are divided into uniform blocks, and each block is labeled with a block number. Based on the center point coordinates, range dimension, and azimuth dimension of the radar illumination area, obtain the vertex coordinates of the four vertices of the radar illumination area, and obtain the maximum and minimum values ​​among the vertex coordinates; based on the maximum and minimum values, determine the block covered by the radar illumination area, and obtain the block number of the covered block.

[0011] Furthermore, when obtaining a multi-attribute digital grid model of the surface based on segmentation and meshing of the DEM digital elevation image and the composite surface attribute partitioning map, the method further includes: Based on the block number, the corresponding blocks are extracted from the DEM digital elevation image and the composite surface attribute division map, and the extracted blocks are read and merged to obtain the extracted elevation image and the extracted attribute division map. Redundancy is removed from the extracted elevation image and the extracted attribute partitioning map, and the extracted elevation image and the extracted attribute partitioning map are then meshed. Obtain the grid coordinates of each grid point in the grid, and obtain the pixel unit elevation value and physical surface information of the surface element corresponding to the grid coordinates based on the nearest neighbor interpolation method; The pixel unit elevation value and the physical surface information are stored in a two-dimensional array to obtain the multi-attribute digital grid model of the land surface.

[0012] Further, the actual operating parameters of the radar are obtained, and the actual operating parameters are transformed into a local coordinate system based on the coordinate transformation matrix to obtain the local operating parameters of the current surface element; the scattered field of the single surface element corresponding to the current surface element is obtained based on the local operating parameters; when traversing all surface elements to obtain the scattered field of the single surface element of all surface elements, the process includes: Based on the maximum feasible step size, the actual frequency band in the actual working condition parameters is rounded to obtain the rounded frequency band; the multi-attribute digital grid model of the land surface is imported, and the physical surface information is obtained; based on the rounded frequency band and the physical surface information, the multi-attribute composite surface electromagnetic feature database is queried to obtain a preliminary scattered field file set; Based on the coordinate transformation matrix, the beam information corresponding to the incident wave vector actually emitted by the radar is mapped from the global geodetic coordinate system to the local coordinate system of the current surface element; based on the transformed incident wave vector components, the local incident elevation angle and local incident azimuth angle in the local coordinate system are obtained from the actual incident elevation angle and the actual incident azimuth angle in the actual operating condition parameters.

[0013] Further, the actual operating parameters of the radar are obtained, and the actual operating parameters are transformed into a local coordinate system based on the coordinate transformation matrix to obtain the local operating parameters of the current surface element; the scattered field of the single surface element corresponding to the current surface element is obtained based on the local operating parameters; when traversing all surface elements to obtain the scattered field of the single surface element of all surface elements, the method further includes: Based on the maximum feasible step size, the local incident elevation angle and the local incident azimuth angle are rounded to obtain the rounded incident elevation angle and the rounded incident azimuth angle. Based on the rounded incident elevation angle and the rounded incident azimuth angle, the corresponding scattered field data file is selected from the preliminary scattered field file set, and the scattered field of the single surface element corresponding to the current surface element is extracted from the selected scattered field data file; Traverse all face elements and repeat coordinate transformation, rounding, filtering and extraction operations to obtain the individual face element scattering field of all face elements.

[0014] Furthermore, when obtaining the total scattering field of each of the imaging resolution units, the following steps are included: Based on the coherent superposition method, the scattering fields of each individual surface element in the same imaging resolution unit are summed to obtain the total scattering field.

[0015] Furthermore, when obtaining the RCS, one-dimensional range profile, and two-dimensional SAR image based on the total scattered field, the process includes: The total scattered fields are superimposed, and the square of the superposition result is taken to obtain the RCS of the entire region where the target is located. The total scattered fields are merged along the azimuth direction, and the merged result is stored in a one-dimensional array to obtain the one-dimensional range image; The total scattered fields are stored in a two-dimensional array according to their spatial location to obtain the two-dimensional SAR image.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: By pre-setting non-polarization parameter ranges and polarization type sets, the maximum feasible step size is determined using a bisection iterative method, and a combination of working condition parameters is generated. This completes the pre-calculation of the scattering field of a single surface element and the construction of a multi-attribute composite surface electromagnetic feature database. Simultaneously, by combining DEM digital elevation images and composite surface attribute partitioning maps, grid alignment, clipping, segmentation, and grid partitioning are achieved, rapidly generating a multi-attribute surface digital grid model. Then, the scattering field of each surface element is obtained through coordinate transformation and parameter rounding lookup tables. After coherent superposition, the total scattering field of the imaging resolution unit is obtained, and finally, the RCS, one-dimensional range image, and two-dimensional SAR image are output. The overall method can significantly reduce the real-time computation of electromagnetic scattering of complex surfaces and greatly improve computational efficiency. At the same time, it takes into account the computational accuracy under multiple surface types and multiple working condition parameters, effectively solving the problems of complex modeling, slow computation, and insufficient accuracy of traditional methods in complex undulating surfaces and multi-attribute composite scenes. It can quickly, stably, and accurately generate multi-attribute composite surface electromagnetic characteristics, meeting the needs of practical engineering applications such as radar detection, geomorphological exploration, and target recognition for efficient and high-precision electromagnetic simulation. Attached Figure Description

[0017] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart of a method for rapidly generating multi-attribute composite surface electromagnetic properties provided in an embodiment of the present invention; Figure 2 This invention provides a DEM (Digital Elevation Model) image of the terrain at a location in Guangzhou. Figure 3 A composite surface attribute classification map of a certain terrain in Guangzhou City provided for an embodiment of the present invention; Figure 4 This is a one-dimensional distance image of the terrain at a certain location in Guangzhou City provided in an embodiment of the present invention; Figure 5 A two-dimensional SAR image of the terrain of a certain location in Guangzhou City, provided for an embodiment of the present invention. Detailed Implementation

[0018] Exemplary embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the invention to those skilled in the art. It should be noted that, without conflict, the embodiments and features described herein can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] It is important to note that: The non-polarization parameter range refers to the range of values ​​for operating parameters other than polarization in radar electromagnetic waves. It is used for parameter discrete sampling during database construction. By presetting the range of values ​​for frequency, incident elevation angle, and incident azimuth angle according to the actual working capabilities of the radar, this range is the non-polarization parameter range.

[0020] The polarization type set refers to the summary of the polarization modes of radar electromagnetic waves, which is used to combine with non-polarization parameters to form operating conditions. The set is formed by preset polarization modes such as HH, VV, HV, and VH, and this set is the polarization type set.

[0021] The binary iterative method is an iterative method used to find the maximum parameter step size that satisfies the required accuracy, and is used to optimize database sampling efficiency. The iterative process of continuously taking intermediate step sizes to verify accuracy and then iteratively converging is called the binary iterative method.

[0022] The maximum feasible step size refers to the maximum parameter sampling interval that meets the accuracy requirements of electromagnetic scattering calculations. It is used for discrete sampling in the database. The maximum step size that meets the accuracy requirements is verified and determined by the binary iterative method. This step size is the maximum feasible step size.

[0023] Operating parameter combination refers to the radar operating parameter group consisting of frequency, polarization, elevation angle, and azimuth angle, which is used for database indexing and scattered field calculation. The parameter group is obtained by combining the Cartesian product of non-polarization parameters and polarization type. This parameter group is the operating parameter combination.

[0024] Non-polarization parameters refer to radar parameters such as frequency, elevation angle, and azimuth angle that do not involve polarization, and are used to construct the combination of operating parameters. The values ​​are obtained by sampling within a preset non-polarization parameter range at a set step size; these values ​​constitute the non-polarization parameters. The non-polarization parameter set refers to the summary of non-polarization parameters obtained by sampling at the maximum feasible step size, used for Cartesian product combination. The parameter set is obtained by discretely sampling the non-polarization parameter range; this set constitutes the non-polarization parameter set.

[0025] Polarization type refers to the type of electric field vibration direction of electromagnetic waves, used to distinguish the scattering characteristics under different polarizations; specific types such as HH and VV are selected from the set of polarization types, and this type is the polarization type.

[0026] Cartesian product combination refers to the method of pairing multiple types of parameters to form all combinations, which is used to generate complete operating conditions; the combination method of cross-pairing non-polar parameters with polarization types is called Cartesian product combination.

[0027] The land surface type set refers to the summary of land surface types such as soil, sand, grassland, forest, cement, and asphalt, which is used for database classification. Six type sets are preset according to the actual physical properties of the land surface, and this set is the land surface type set.

[0028] A multi-attribute composite surface electromagnetic feature database refers to a data set that stores the scattered fields of various surface types under different operating conditions, used for rapid table lookup; the scattered fields of individual surface elements are stored according to the combination of surface type and operating condition, and this data set constitutes the database.

[0029] The electromagnetic feature sub-database refers to a branch of the database divided according to a single land surface type, used for classified storage and indexing; the database is split into sub-databases according to the number of land surface types, and these sub-databases are the electromagnetic feature sub-databases.

[0030] A scattering field data file is a file that stores the scattering field of a single surface element under a set of operating conditions and is used for data access. The file is named after the operating conditions and stores the scattering field data.

[0031] Interpolation encryption refers to the operation of interpolating DEM data to improve resolution and enhance grid accuracy; performing bilinear interpolation on DEM digital elevation images to increase pixel density is called interpolation encryption.

[0032] Mesh generation refers to the operation of dividing a surface area into small facets, which is used to construct a digital mesh model; dividing a surface area into uniform small facets according to size is called mesh generation.

[0033] A multi-attribute digital grid model refers to a set of surface elements with elevation and surface attributes, used for scattering field calculations. After alignment, clipping, and partitioning, an attributed surface element model is obtained, which is the digital grid model.

[0034] The pixel unit elevation value refers to the ground elevation value corresponding to each pixel in a DEM image, which is used to construct terrain undulations; the pixel height value is read from the DEM digital elevation image.

[0035] Georeferenced information refers to the coordinate reference data used for positioning in a DEM (Digital Image Model), which is used for pixel latitude and longitude calculations. The projection and coordinate reference information read from the DEM image is georeferenced information.

[0036] The latitude and longitude coordinates of a pixel unit refer to the geographic latitude and longitude corresponding to a DEM pixel, which are used for image alignment. The latitude and longitude of a pixel are calculated based on the DEM geographic reference information, and these coordinates are the latitude and longitude coordinates of the pixel unit.

[0037] RGB color refers to the color value used to distinguish land surface types in an attribute partitioning map, and is used to identify land surface attributes; the pixel RGB value is read from the composite land surface attribute partitioning map, and this color value is the RGB color.

[0038] Corner point latitude and longitude coordinates refer to attribute division Figure 4 The latitude and longitude of each corner point are used for image registration; the geographic coordinates of the corner points are read from the composite surface attribute map, and these coordinates are the latitude and longitude coordinates of the corner points.

[0039] A block refers to a uniform small patch after the DEM and attribute map are segmented, which is used to quickly extract the radar illumination area; a large image is segmented into small patches of a fixed size, and these patches are called blocks.

[0040] Block number refers to the sequence number assigned to the divided blocks for quick location and retrieval; the divided blocks are numbered sequentially, and this number is the block number.

[0041] Center point coordinates refer to the latitude and longitude / planar coordinates of the center of the radar illumination area, used to determine the illumination area; obtain the geographic coordinates corresponding to the radar beam center, which are the center point coordinates.

[0042] Range dimension refers to the length of the radar illumination area along the range direction, used to determine the illumination range; the range length is set according to the radar detection requirements, and this length is the range dimension.

[0043] The azimuth dimension refers to the width of the radar illumination area along the azimuth direction, which is used to determine the illumination range. The azimuth width is set according to the radar detection requirements, and this width is the azimuth dimension.

[0044] Elevation image extraction refers to the extraction of radar-illuminated area elevation sub-maps from the DEM for modeling purposes; DEM blocks are extracted and merged according to block number, and this sub-map is the extracted elevation image.

[0045] The extracted attribute partition map refers to the surface attribute sub-map of the radar-illuminated area extracted from the attribute map, which is used for modeling; the attribute map blocks are extracted and merged according to the block number, and this sub-map is the extracted attribute partition map.

[0046] Redundancy removal refers to the operation of increasing the grid resolution to meet imaging accuracy; increasing the grid resolution of the extracted image by a set factor is the operation of redundancy removal.

[0047] Grid coordinates refer to the planar coordinates of each element after subdivision, used to assign elevation and attributes; the coordinates of the elements are calculated according to the radar direction and size, and these coordinates are the grid coordinates.

[0048] Nearest neighbor interpolation is an interpolation method that takes the nearest pixel value from the original image for quick assignment; it takes the nearest pixel elevation and attribute value in the grid coordinates.

[0049] Physical surface information refers to the land surface type attribute corresponding to a surface element, which is used to match the sub-database of the database; the surface type of the surface element is obtained from the attribute partitioning map, and this information is the physical surface information.

[0050] Azimuth direction refers to the direction in which the radar beam is perpendicular to the range, and is used for one-dimensional range image synthesis; the lateral direction of radar beam scanning is the azimuth direction.

[0051] Range direction refers to the radial direction of radar wave propagation, used for range resolution; the radial direction of radar wave transmission and reception is the range direction.

[0052] The radar illumination area refers to the area of ​​the ground covered by the radar beam, which is used to determine the modeling area. The area is defined based on the center point, range, and azimuth dimensions, and this area is the radar illumination area.

[0053] Actual operating parameters refer to the frequency, polarization, elevation angle, and azimuth angle of the radar during actual operation, which are used for table lookup calculations; input the actual operating parameter set of the radar, and this parameter set is the actual operating parameters.

[0054] A coordinate transformation matrix is ​​a mathematical matrix that enables the transformation between global and local coordinate systems and is used for angle transformation. The transformation matrix is ​​constructed based on the normal and orientation of the surface element, and this matrix is ​​the coordinate transformation matrix.

[0055] Local coordinate system transformation refers to the operation of converting global parameters to the coordinate system of the surface element itself, which is used to calculate local angles; the operation of mapping global quantities to the local surface element using a coordinate transformation matrix is ​​called local coordinate system transformation.

[0056] Local operating parameters refer to the incident angle parameters in the local coordinate system of the surface element, which are used for array indexing; the local incident parameters of the surface element are obtained through coordinate transformation, and these parameters are the local operating parameters.

[0057] A surface element is the smallest computational unit in a digital grid model, used for scattering field calculations; the smallest surface unit obtained by grid partitioning is a surface element.

[0058] The actual frequency band refers to the actual operating frequency of the radar, which is used for database rounding and matching; input the actual operating frequency value of the radar, and that frequency value is the actual frequency band.

[0059] The rounded frequency band refers to the frequency after rounding according to the database step size, which is used for database file name matching; the actual frequency band is rounded according to the maximum feasible step size, and the result is the rounded frequency band.

[0060] The preliminary scattering field file set refers to a group of data files matched with the surface type according to the rounded frequency band, which is used for subsequent indexing; the file set obtained by querying based on the rounded frequency band and physical surface information is the preliminary scattering field file set.

[0061] The incident wave vector is a vector that describes the direction of the radar incident wave and is used for coordinate transformation. The incident direction vector is determined based on the radar elevation and azimuth angles, and this vector is the incident wave vector.

[0062] The global geodetic coordinate system refers to a unified coordinate system based on the earth and used for global positioning; the geographic / planar coordinate system used uniformly throughout the entire scene is the global geodetic coordinate system.

[0063] A local coordinate system is a coordinate system based on the surface element itself, used to calculate local angles; a local coordinate system is a coordinate system established by the plane and normal of the surface element.

[0064] The actual incident elevation angle refers to the radar's incident elevation angle in the global context, used for coordinate transformation; input the actual incident elevation angle value of the radar, and this angle value is the actual incident elevation angle.

[0065] The actual incident azimuth angle refers to the radar incident azimuth angle in the global context, used for coordinate transformation; input the actual incident azimuth angle value of the radar, and this angle value is the actual incident azimuth angle.

[0066] The local incident elevation angle refers to the local incident elevation angle of a surface element, used for array indexing; the local elevation angle is calculated through coordinate transformation, and this angle is the local incident elevation angle.

[0067] The local incident azimuth angle refers to the incident azimuth angle of a local area of ​​a surface element, and is used for array indexing. The local azimuth angle is calculated through coordinate transformation, and this angle is the local incident azimuth angle.

[0068] The rounded incident pitch angle is the local pitch angle after rounding up by the step size, which is used for positioning array index; the local pitch angle is rounded up by the maximum feasible step size, and this angle is the rounded incident pitch angle.

[0069] The rounded incident azimuth angle is the local azimuth angle after rounding up the step size, which is used to locate the array index; the local azimuth angle is rounded up according to the maximum feasible step size, and this angle is the rounded incident azimuth angle.

[0070] Imaging range refers to the surface area from which RCS, SAR, and other results are generated, and is used to divide the resolution units; the range from which the results are generated is determined based on the radar illumination area, and this range is the imaging range.

[0071] An imaging resolution cell refers to an imaging grid divided according to radar resolution, used for superimposing scattered fields; the imaging range is divided into small cells according to radar resolution, and these cells are called imaging resolution cells.

[0072] The total scattered field refers to the sum of the scattered fields of all surface elements within the same resolution cell, and is used to generate the result; the total scattered field is the result of the coherent superposition of the scattered fields of the surface elements within the cell.

[0073] RCS refers to the radar cross section of the entire region, used to characterize the scattering intensity; the square of the modulus of the sum of the total scattered fields of all imaging units is the result of RCS.

[0074] A one-dimensional range profile refers to the range-direction scattering intensity distribution, used for radar target identification. The total scattering field along the azimuth direction is combined and stored in a one-dimensional array, which is the one-dimensional range profile.

[0075] A two-dimensional SAR image is a two-dimensional scattering distribution image of azimuth and range, used for surface imaging; the total scattered field is stored in a two-dimensional array according to spatial location, and this array is the two-dimensional SAR image.

[0076] Coherent superposition is a method of directly adding complex scattered fields vector by vector to synthesize the total scattered field; it is the method of directly adding the complex scattered fields of surface elements within the same resolution unit.

[0077] The scattering field of a single surface element refers to the electromagnetic scattering field value of a single tiny surface element under corresponding working conditions, which is used to constitute the total scattering field of each resolution unit. The scattering field value of the surface element is obtained by pre-calculating the working condition parameters and surface type in the database, and this value is the scattering field of a single surface element.

[0078] A digital elevation image (DEM) is a digital image that contains information about the elevation of the Earth's surface. It is used to provide elevation values ​​for pixel units to build a terrain model. The surface elevation distribution image of the area to be surveyed is obtained by acquiring and analyzing historical topographic mapping data. This image is the DEM.

[0079] A composite land surface attribute classification map is an image that uses RGB colors to distinguish different land surface types, providing physical surface information for each land surface. The image of the distribution of land surface types in the area to be measured is obtained by acquiring and analyzing historical remote sensing data on land surface classification. This image is the composite land surface attribute classification map.

[0080] Error threshold refers to the maximum permissible error value for determining whether the accuracy of the scattered field calculation meets the requirements. It is used in the binary iteration to determine whether the initial maximum feasible step size is compliant. The maximum permissible error critical value that meets the accuracy of engineering simulation is obtained by acquiring and analyzing historical data of electromagnetic scattering numerical calculation. This critical value is the error threshold.

[0081] The non-polarization parameter ranges include: frequency band range, incident elevation angle range, and incident azimuth angle range; the polarization type set includes: HH, VV, HV, and VH.

[0082] Non-polarization parameters include: frequency band, incident elevation angle, and incident azimuth angle.

[0083] The set of land surface types includes: soil, grassland, woodland, cement, and asphalt.

[0084] Reference Figure 1 As shown in some embodiments of this application, a method for rapidly generating multi-attribute composite surface electromagnetic properties includes: Step 1: Preset the non-polarization parameter range and polarization type set of radar electromagnetic waves, and obtain the maximum feasible step size of each non-polarization parameter range based on the binary iterative method; obtain mutually independent combinations of operating parameters based on each maximum feasible step size.

[0085] Specifically, when obtaining the maximum feasible step size for each of the aforementioned non-polarization parameter intervals based on the bisection iteration method, the following steps are included: A precise step size and a coarse step size are preset for a non-polarization parameter range, and a step size range is constructed based on the precise step size and the coarse step size; the median value of the step size range is used as the initial maximum feasible step size. Preset conventional typical operating condition values ​​for the surface type, polarization type and other non-polarization parameters respectively; Based on the precise step size, the current non-polarization parameter range is discretely sampled, and the individual surface element scattering field corresponding to each sampling point is obtained based on the collected non-polarization parameters and the conventional typical working condition values. Based on the initial maximum feasible step size, the current non-polarization parameter range is discretely sampled, and the individual surface element scattering field corresponding to each sampling point is obtained based on the collected non-polarization parameters and the conventional typical working condition values. Based on the precise step size and interpolation fitting algorithm, the scattering field of the single surface element corresponding to the initial maximum feasible step size is interpolated and supplemented; The interpolated and supplemented scattering fields of each individual surface element are compared one by one with the scattering fields of each individual surface element corresponding to the precision step size, and the average error of the scattering field between the two sets of individual surface element scattering fields is obtained. When the average error of the scattered field is less than or equal to the preset error threshold, the lower limit of the step size interval is updated to the initial maximum feasible step size, and a binary iterative operation is performed based on the new step size interval to obtain the average error of the scattered field. The step size interval is updated repeatedly until the average error of the scattered field is greater than the error threshold. The last compliant initial maximum feasible step size is taken as the maximum feasible step size. When the average error of the scattered field is greater than the preset error threshold, the upper limit of the step size interval is updated to the initial maximum feasible step size, and a binary iterative operation is performed based on the new step size interval to obtain the average error of the scattered field. The step size interval is updated repeatedly until the average error of the scattered field is less than or equal to the error threshold. The first compliant initial maximum feasible step size is taken as the maximum feasible step size. For the other non-polarization parameter intervals, a binary iterative method is performed to obtain the corresponding maximum feasible step size.

[0086] Understandably, this method works in synergy based on the bisection interval convergence principle, the electromagnetic scattering numerical calculation accuracy control principle, the interpolation fitting data reconstruction principle, and the error quantitative assessment principle. It constructs a closed step size search interval by setting precise and coarse step sizes for the non-polarization parameter interval. The intermediate value of the interval is used as the initial maximum feasible step size to achieve rapid binary positioning of the step size candidate value. A unified scattering field calculation benchmark is established based on typical working conditions of surface type, polarization type, and other non-polarization parameters to ensure the comparability of calculation results under different step sizes. A precise step size is used to complete high-density discrete sampling of the parameter interval to obtain high-fidelity reference scattering field data. A low-density sampling is performed using the initial maximum feasible step size to reduce computational overhead. The interpolation fitting algorithm is then used based on the high-density... The sampling points are used to complete the low-density results, ensuring that the two sets of scattered fields are consistent in sampling dimension and position for accurate comparison. The average error of the scattered field is used to quantify the calculation deviation under different step lengths. A preset error threshold is used as the criterion for judging the effectiveness of the step length. The step length interval boundary is dynamically adjusted by combining a binary iterative strategy. When the error meets the constraint, the lower limit of the step length interval is increased to try a larger step length. When the error exceeds the constraint, the upper limit of the step length interval is shrunk to reduce the step length value. Through multiple iterations, the maximum feasible step length that meets the accuracy constraint is gradually approximated and determined. This process can be extended to various non-polarized parameter intervals such as frequency, incident elevation angle, and incident azimuth angle, so that the electromagnetic feature database can obtain the maximum feasible step length corresponding to each parameter while ensuring the accuracy of the scattering values.

[0087] For example, frequency is selected as the non-polarization parameter range, with a value range of 8GHz to 18GHz. The preset precision step size is 0.1GHz and the coarse step size is 2GHz, constructing an initial step size range of [0.1GHz, 2GHz]. The initial maximum feasible step size is set to the median value of 1.05GHz. The surface type is set to cement, the polarization type to HH polarization, and other typical non-polarization parameters are an incident elevation angle of 30° and an incident azimuth angle of 90°. Based on a precise step size of 0.1GHz, the frequency range of 8GHz to 18GHz is discretely sampled, and the scattering field of each sampled element is calculated. The scattering field is then sampled again with a preliminary maximum feasible step size of 1.05GHz. A linear interpolation fitting algorithm is used to interpolate and supplement the low-sampling-rate scattering field data. The supplemented data is compared with the precise step size reference data, and the average error of the scattering field is calculated to be 0.03. This value is less than the preset error threshold of 0.05. Therefore, the lower limit of the step size range is updated to... The first step is 1.05 GHz, and the new interval becomes [1.05 GHz, 2 GHz]. The second step is to take the median value of 1.525 GHz as the initial maximum feasible step size. The sampling, calculation, interpolation, and comparison process is repeated, and the average error is 0.04, which is still less than 0.05. The lower limit of the step size interval is updated to 1.525 GHz, and the new interval becomes [1.525 GHz, 2 GHz]. The third step is to take the median value of 1.7625 GHz as the initial maximum feasible step size. The average error is calculated to be 0.06, which is greater than 0.05. At this point, the iteration stops, and the last 1.525 GHz that meets the accuracy requirements is determined as the maximum feasible step size of the frequency parameter interval. The same iteration method is used to calculate the incident elevation angle interval 0°~90° and the incident azimuth angle interval 0°~360°, and the maximum feasible step size of the incident elevation angle is 1° and the maximum feasible step size of the incident azimuth angle is 2°, respectively. The maximum feasible step size of all non-polarization parameter intervals is obtained.

[0088] Specifically, when obtaining independent combinations of operating parameters based on each of the maximum feasible step sizes, the following are included: Based on the maximum feasible step size, each of the non-polarization parameter intervals is discretely sampled, and a set of non-polarization parameters corresponding to each of the non-polarization parameter intervals is constructed based on the collected non-polarization parameters. By combining the non-polar parameters in each set of non-polar parameters and the polarization types in the set of polarization types, several sets of independent working condition parameter combinations are obtained. Understandably, this approach relies on the principles of parameter discretization, set completeness, and Cartesian product combinatorial principles. It involves uniformly discretizing non-polar parameter ranges (frequency, incident elevation angle, incident azimuth angle, etc.) using the maximum feasible step size corresponding to each determined non-polar parameter range. This results in a dimensionally regular and non-redundant set of non-polar parameters, ensuring that elements within each set are independent and cover the entire parameter range. The Cartesian product combinatorial rule is then used to perform full permutations and combinations on multiple non-polar parameter sets and polarization type sets, enabling seamless and non-repeating cross-matching between different parameter types, generating a comprehensive set. The combination of operating parameters in the form of parameter pairings ensures that each set of operating parameter combinations is independent in terms of parameter values ​​and types, with no duplication or inclusion relationships. At the same time, relying on the constraint of the maximum feasible step size, the number of generated operating parameter combinations is kept to the minimum within the accuracy allowable range. This ensures that the database covers all required working states, while avoiding data expansion and decreased retrieval efficiency due to oversampling. The entire process is based on parameter space gridding and full combination mapping mechanism, which transforms the multi-dimensional continuous parameter space into a finite, regular, and independent set of discrete operating point points, providing a stable parameter foundation for the subsequent structured storage and fast indexing of the electromagnetic feature database.

[0089] For example, the non-polarization parameter range includes a frequency range of 8 GHz to 18 GHz, an incident elevation angle range of 0° to 90°, and an incident azimuth angle range of 0° to 360°, with corresponding maximum feasible step sizes of 1.525 GHz, 1°, and 2°, respectively. Based on these maximum feasible step sizes, each non-polarization parameter range is discretized to obtain the set of frequency non-polarization parameters {8.000 GHz, 9.525 GHz, 11.050 GHz, 12.525 ... GHz, 12.575GHz, 14.100GHz, 15.625GHz, 17.150GHz}, incident elevation angle non-polarization parameter set {0°, 1°, 2°, ..., 89°, 90°}, incident azimuth angle non-polarization parameter set {0°, 2°, 4°, ..., 358°, 360°}, polarization type set {HH, VV, HV, VH}, The frequency, incident elevation angle, incident... The three sets of non-polarized parameters (azimuth, pitch, and azimuth) are sequentially combined using Cartesian products to ensure that each frequency, elevation, and azimuth value is matched pairwise without omission or repetition. Then, the resulting three-dimensional non-polarized parameter combinations are combined with the four polarization types from the polarization type set using a full-dimensional Cartesian product. This results in independent and non-redundant combinations of operating parameters in the form of {8.000GHz, 0°, 0°, HH}, {8.000GHz, 0°, 0°, VV}, {8.000GHz, 0°, 0°, HV}, {8.000GHz, 0°, 0°, VH}, {8.000GHz, 0°, 2°, HH}, and {8.000GHz, 0°, 2°, VV}. All combinations have unique parameter values ​​and can be directly used for the structured construction and indexing of multi-attribute composite surface electromagnetic feature databases.

[0090] Step 2: Preset a set of land surface types, and perform numerical calculations of the scattering field of a single surface element for each land surface type in the set based on the combination of operating parameters; construct a multi-attribute composite land surface electromagnetic feature database based on the land surface type, the combination of operating parameters, and the scattering field of the single surface element.

[0091] Specifically, when constructing a multi-attribute composite surface electromagnetic feature database based on the surface type, the combination of operating parameters, and the scattering field of a single surface element, the following is included: The multi-attribute composite surface electromagnetic feature database is divided into a corresponding number of electromagnetic feature sub-databases based on the number of surface types, and the electromagnetic feature sub-databases are named based on the surface types. For each set of operating condition parameter combinations, a scattering field data file is created in each of the electromagnetic feature sub-libraries; The scattered field data file is named based on the combination of operating parameters, and the scattered field of a single surface element is stored in the corresponding scattered field data file based on the surface type and the combination of operating parameters. Using the surface type as the primary index and the combination of operating parameters as the secondary index, a joint index marker is established for the scattering field of each individual surface element in the multi-attribute composite surface electromagnetic feature database based on the primary index and the secondary index.

[0092] Understandably, this system is implemented based on the principles of hierarchical database storage, structured indexing, and data mapping matching. It divides the database into sub-databases according to the number of surface types and names them by surface type, forming a physically independent and logically clearly categorized storage structure. Each electromagnetic feature sub-database is created with an independent scattered field data file based on combinations of operating parameters, ensuring that the scattered field data corresponding to each set of parameters has a dedicated storage medium. Naming the scattered field data files using combinations of operating parameters establishes a one-to-one mapping relationship between parameters and data files, guaranteeing the uniqueness and accuracy of data storage and retrieval. The scattered field of a single surface element is written into the corresponding data file according to the surface type and operating parameter combination. According to the document, accurate matching between data and storage location is achieved. A joint index tag is constructed with surface type as the primary index and combination of operating parameters as the secondary index. The hierarchical retrieval mechanism of multi-level indexes greatly improves the efficiency of data location. Through layered partitioning, independent storage, accurate mapping and the synergistic effect of multi-level indexes, discrete scattering field data is transformed into a structured and fast-searchable database system. This ensures that target data can be quickly located based on surface attributes and radar operating conditions during subsequent table lookup calculations. At the same time, it ensures that the database has good compatibility and maintainability when expanding surface types and operating parameters, realizing standardized management and efficient access to multi-type and multi-operating-condition scattering data.

[0093] For example, the land surface type set includes six types: {soil, sand, grassland, woodland, cement, and asphalt}. Based on this, the multi-attribute composite land surface electromagnetic feature database is divided into six electromagnetic feature sub-databases, named soil, sand, grassland, woodland, cement, and asphalt sub-databases, respectively. Multiple independent combinations of operating parameters are selected, such as {8.000GHz, 0°, 0°, HH}, {8.000GHz, 0°, 0°, VV}, and {9.525GHz, 1°, 2°, HH}. Numerical calculations of the scattering field of a single surface element are performed for each standard surface element under each operating parameter combination. The specific calculation process is as follows: First, a fixed simulation distance of 1000m and a standard incident wave are determined. The base field strength was 1 V / m. Inherent physical parameters corresponding to different land surface types were retrieved: soil: relative permittivity 4.5, conductivity 0.001 S / m, root mean square roughness 0.008 m; sandy land: relative permittivity 3.8, conductivity 0.0008 S / m, root mean square roughness 0.012 m; grassland: relative permittivity 3.2, conductivity 0.0005 S / m, root mean square roughness 0.02 m; forest land: relative permittivity 2.8, conductivity 0.0003 S / m, root mean square roughness 0.03 m; cement: relative permittivity 6.0, conductivity 0.002 S / m, root mean square roughness 0.003 m; asphalt: relative permittivity 5.5, conductivity 0.0015 S / m. With a root mean square roughness of 0.004 m, and considering the radar frequency, incident angle, and polarization mode corresponding to each working condition, a surface element electromagnetic scattering simulation model is used. Electromagnetic wave propagation loss, surface dielectric loss, and roughness scattering modulation coefficient are substituted into the model for step-by-step calculation. First, the electromagnetic wave number and propagation attenuation are calculated. Then, the backscattering amplitude is solved by combining the surface polarization scattering response coefficient. Simultaneously, the corresponding polarization phase offset is matched to obtain the phase value. Finally, complex form scattering field data of a single surface element with specific values ​​is synthesized. For example, a cement surface element with 8.000 GHz, 0° incident angle, and HH polarization can be calculated with a precise numerical result of a scattering field amplitude of 0.82 V / m and a phase of 15.6°. This is achieved in each electromagnetic feature sub-library. For each set of operating parameters, a corresponding scattered field data file is created. The data files are named according to the operating parameter combination. For example, the file corresponding to {8.000GHz, 0°, 0°, HH} is named 8GHz_0deg_0deg_HH, and the file corresponding to {9.525GHz, 1°, 2°, HH} is named 9.525GHz_1deg_2deg_HH. The calculated precise single-element scattered field numerical data corresponding to different surface types and operating conditions are stored in the corresponding named data files to ensure that each data file stores uniquely matching standard scattered field numerical data. Cement, soil, and other surface types are used as the first-level index of the database, and 8 is used as the index.Combinations of operating parameters such as 000GHz, 0°, 0°, and HH are used as secondary indexes. Based on the primary and secondary indexes, a unique joint index label is established for the scattering field of each individual surface element, enabling bidirectional correlation retrieval between surface attributes and radar operating parameters. This ultimately forms a multi-attribute composite surface electromagnetic feature database with standardized data storage, a clear indexing system, and rapid retrieval capabilities.

[0094] Step 3: Obtain the DEM digital elevation image and composite surface attribute map of the entire area where the target is located; perform grid alignment and cropping on the DEM digital elevation image and the composite surface attribute map; perform interpolation and densification on the DEM digital elevation image; obtain a multi-attribute digital grid model of the surface based on the segmentation and grid partitioning of the DEM digital elevation image and the composite surface attribute map.

[0095] Specifically, when performing grid alignment and cropping on the DEM digital elevation image and the composite surface attribute map, the following steps are included: The DEM digital elevation image includes pixel unit elevation values ​​and geographic reference information; the latitude and longitude coordinates of the pixel units of the DEM digital elevation image are obtained based on the geographic reference information, and the land surface type is divided into the whole area based on the RGB colors in the composite land surface attribute division map; Obtain the latitude and longitude coordinates of the corner points of the composite surface attribute division map, and perform grid alignment between the composite surface attribute division map and the DEM digital elevation image based on the latitude and longitude coordinates of the corner points and the latitude and longitude coordinates of the pixel units; The portion of the DEM digital elevation image that extends beyond the composite surface attribute delineation map is cropped.

[0096] Understandably, based on the principles of geospatial registration, pixel coordinate mapping, and image spatial cropping, the pixel unit elevation values ​​and georeferenced information contained in the DEM digital elevation image provide a basis for surface elevation and spatial positioning. Relying on the georeferenced information, the pixel planar coordinates of the DEM image can be converted into geographic latitude and longitude coordinates. The RGB color values ​​in the composite surface attribute division map have a fixed correspondence with surface types, allowing for the spatial division of surface attributes for the entire region based on RGB values. The latitude and longitude coordinates of the corner points in the composite surface attribute division map provide a benchmark reference for spatial registration. Through bidirectional matching of corner point coordinates and DEM pixel unit latitude and longitude coordinates, it is possible to... This method enables pixel-by-pixel grid alignment between two images in geospatial space, eliminating data errors caused by pixel offset and spatial misalignment. It ensures a one-to-one correspondence between elevation information and surface attribute information for the same geographical location. Based on spatial range consistency constraints, areas in the DEM image that exceed the coverage of the composite surface attribute map are cropped, ensuring that the effective spatial ranges of the two images completely overlap. This avoids invalid areas from participating in subsequent calculations. The entire process achieves precise spatial matching of elevation information and surface attribute information through layers of constraints, including geographic coordinate unification, pixel spatial alignment, and redundant area removal. This provides spatially consistent and accurate basic data support for the subsequent construction of digital grid models.

[0097] For example, the DEM digital elevation image and composite surface attribute map of the entire area where the target is located are obtained as follows: Figure 2 and Figure 3As shown; the elevation values ​​of the pixel units in the DEM digital elevation image range from 0m to 200m. The geographic reference information uses the WGS84 coordinate system. Based on this geographic reference information, the latitude and longitude coordinates of each pixel unit in the DEM image are calculated to be between 113.2° and 113.4° east longitude and between 23.1° and 23.3° north latitude. The composite surface attribute classification map uses RGB colors to distinguish surface types, where RGB(128,64,64) corresponds to soil, RGB(244,164,96) corresponds to sandy land, RGB(34,139,34) corresponds to grassland, RGB(0,100,0) corresponds to forest land, RGB(128,128,128) corresponds to cement, and RGB(169,169,169) corresponds to asphalt. Based on this, the following is completed. The overall land surface type classification and the latitude and longitude coordinates of the corner points of the composite land surface attribute classification map are as follows: upper left corner (113.2°, 23.3°), upper right corner (113.4°, 23.3°), lower left corner (113.2°, 23.1°), and lower right corner (113.4°, 23.1°). The latitude and longitude coordinates of these corner points are matched point by point with the latitude and longitude coordinates of the DEM pixel units to achieve pixel-level grid alignment between the composite land surface attribute classification map and the DEM digital elevation image. Then, using the latitude and longitude range of the composite land surface attribute classification map as the boundary, all redundant areas in the DEM digital elevation image that exceed the range of 113.2° to 113.4° east longitude and 23.1° to 23.3° north latitude are cropped to make the spatial range and pixel distribution of the two images completely consistent.

[0098] Specifically, when interpolating and encrypting the DEM digital elevation image, the following steps are included: The elevation values ​​of the pixel units are interpolated and encrypted based on the pixel resolution of the composite surface attribute map; the interpolation encryption method is bilinear interpolation, and the formula is: Wherein, P11, P21, P12, and P22 are the four nearest original pixels surrounding the point P to be interpolated in the DEM digital elevation image, respectively; P11 is the lower left pixel of point P; P21 is the lower right pixel of point P; P12 is the upper left pixel of point P; and P22 is the upper right pixel of point P; coefficients The weights of the elevation values ​​of the four points surrounding point P are given by the following formula: Where x and y are the horizontal and vertical coordinates of point P in the pixel grid, respectively; x1 and y1 are the horizontal and vertical coordinates of point P11 in the pixel grid, respectively; x2 and y1 are the horizontal and vertical coordinates of point P21 in the pixel grid, respectively; x1 and y2 are the horizontal and vertical coordinates of point P12 in the pixel grid, respectively; and x2 and y2 are the horizontal and vertical coordinates of point P22 in the pixel grid, respectively.

[0099] Specifically, when obtaining a multi-attribute digital grid model of the surface based on segmentation and gridding of the DEM digital elevation image and the composite surface attribute partitioning map, the following steps are included: The DEM digital elevation image and the composite surface attribute map are divided into uniform blocks, and each block is labeled with a block number. Based on the center point coordinates, range dimension, and azimuth dimension of the radar illumination area, obtain the vertex coordinates of the four vertices of the radar illumination area, and obtain the maximum and minimum values ​​among the vertex coordinates; based on the maximum and minimum values, determine the block covered by the radar illumination area, and obtain the block number of the covered block. Based on the block number, the corresponding blocks are extracted from the DEM digital elevation image and the composite surface attribute partitioning map, respectively. The extracted blocks are then read and merged to obtain the extracted elevation image and the extracted attribute partitioning map. Redundancy is removed from the extracted elevation image and the extracted attribute partitioning map, and the extracted elevation image and the extracted attribute partitioning map are then meshed. The grid coordinates of each grid point in the grid are obtained, and the pixel unit elevation value and physical surface information of the surface element corresponding to the grid coordinates are obtained based on the nearest neighbor interpolation method. The pixel unit elevation value and the physical surface information are stored in a two-dimensional array to obtain the multi-attribute digital grid model of the surface.

[0100] Understandably, this method is based on the collaborative construction of principles such as image block segmentation, geographic coordinate boundary constraints, local region extraction and fusion, grid discretization, and spatial interpolation matching. It achieves modular decomposition and pixel spatial positioning of large-scale remote sensing images by uniformly segmenting the registered and cropped DEM digital elevation image and composite surface attribute map into blocks and assigning unique block numbers. The coordinates of the four vertices of the illuminated area are calculated using the center point coordinates, range dimension, and azimuth dimension of the radar-illuminated area. The effective geographic boundary of the radar coverage is locked by extracting the extreme parameters of the vertex coordinates. The precise selection and numbering of target blocks are completed based on the spatial inclusion relationship between the boundary range and the geographic coordinates of each block. Block reading and stitching fusion are performed according to the image data corresponding to the block number, achieving effective local segmentation within the entire image. The simulation area is accurately extracted, and the image grid resolution is optimized by redundancy removal. The spatial accuracy benchmark of the elevation image and attribute image is unified. The continuous surface space is discretized into units through regular grid partitioning, generating a well-distributed grid point coordinate system. Based on the spatial pixel mapping principle of the nearest neighbor interpolation method, the grid coordinates are accurately matched with the original image pixel data, realizing the accurate mapping and assignment of the elevation value of each grid element to the physical surface attribute information. Based on the data structure storage principle of two-dimensional array, the two-dimensional attribute information of the discrete grid unit is encapsulated in an orderly manner, completing the construction of a multi-attribute digital grid model of the surface. The overall process realizes lightweight screening, accuracy optimization and structured modeling of large-scene surface data, ensuring the spatial integrity, data matching and grid regularity of subsequent element scattering field calculations.

[0101] For example, the overall geographical range of the DEM digital elevation image and composite surface attribute map after grid alignment and cropping is 113.2°–113.4°E and 23.1°–23.3°N. Both images are uniformly divided into 0.02° × 0.02° blocks of equal size, and uniquely numbered 1, 2, 3…100 sequentially from left to right and top to bottom. The center point coordinates of the radar illumination area are set to 113.3°E and 23.2°N, and the distance vector is… With a range dimension of 0.1° and an azimuth dimension of 0.1°, the coordinates of the four vertices of the radar illumination area were calculated using coordinate conversion formulas. In the formulas, the center point coordinates are the geographic coordinates of the radar beam center, and the range and azimuth dimensions are the extension scales of the illumination area boundary. The calculated coordinates of the four vertices are (113.25°, 23.25°), (113.35°, 23.25°), (113.25°, 23.15°), and (113.35°, 23.15°). The maximum and minimum values ​​of the vertex coordinates are extracted to the east. Based on coordinates of 113.25°~113.35° and 23.15°~23.25° North latitude, the radar illumination area covering blocks numbered 42, 43, 52, and 53 was determined using maximum / minimum matching. According to these block numbers, the corresponding block data were read from the DEM digital elevation image and the composite surface attribute map, and then stitched together to obtain the extracted elevation image and the extracted attribute map. After performing a 2x redundancy optimization on the two extracted images, a precision of 0.005° × 0.005° was applied. Uniform grid subdivision generates a regular grid coordinate system. The grid coordinates of all grid points are traversed, and the nearest neighbor interpolation method is used to match the original image pixel data. The elevation values ​​(range 0m~200m) and physical surface information (soil, sand, grassland, forest, cement, asphalt) of the corresponding pixel units of each grid element are obtained one by one. Finally, the elevation data and surface attribute information of all grid points are stored in an orderly two-dimensional array. This two-dimensional array is the completed multi-attribute digital grid model of the land surface.

[0102] Specifically, obtaining the grid coordinates of each grid point in the grid includes: When the azimuth angle of the radar beam direction is zero, the grid coordinates are calculated by the following formula: in, , The coordinates of the center point of the irradiated area are: , The length and width of the irradiated area, , The dimensions of the grid along the azimuth and range directions; When the azimuth angle of the radar beam direction is not zero, the coordinates are rotated to the actual direction of the radar wavenumber. The rotated coordinates are: in, This represents the azimuth angle of the radar beam.

[0103] Step 4: Obtain the actual operating parameters of the radar, and perform local coordinate system transformation on the actual operating parameters based on the coordinate transformation matrix to obtain the local operating parameters of the current surface element; obtain the scattering field of the single surface element corresponding to the current surface element based on the local operating parameters; traverse all surface elements to obtain the scattering field of the single surface element of all surface elements.

[0104] Specifically, the process involves acquiring the actual operating parameters of the radar, transforming these parameters into a local coordinate system based on a coordinate transformation matrix to obtain the local operating parameters of the current surface element; acquiring the scattered field of the single surface element corresponding to the current surface element based on the local operating parameters; and iterating through all surface elements to obtain the scattered field of the single surface element of all surface elements, including: Based on the maximum feasible step size, the actual frequency band in the actual working condition parameters is rounded to obtain the rounded frequency band; the multi-attribute digital grid model of the land surface is imported, and the physical surface information is obtained; based on the rounded frequency band and the physical surface information, the multi-attribute composite surface electromagnetic feature database is queried to obtain a preliminary scattered field file set; Based on the coordinate transformation matrix, the beam information corresponding to the incident wave vector actually transmitted by the radar is mapped from the global geodetic coordinate system to the local coordinate system of the current surface element; based on the transformed incident wave vector components, the local incident elevation angle and local incident azimuth angle in the local coordinate system are obtained from the actual incident elevation angle and the actual incident azimuth angle in the actual operating condition parameters. Based on the maximum feasible step size, the local incident elevation angle and the local incident azimuth angle are rounded to obtain the rounded incident elevation angle and the rounded incident azimuth angle; based on the rounded incident elevation angle and the rounded incident azimuth angle, the corresponding scattered field data file is selected from the preliminary scattered field file set, and the scattered field of the single surface element corresponding to the current surface element is extracted from the selected scattered field data file; all surface elements are traversed, and the coordinate transformation, rounding, filtering and extraction operations are repeated to obtain the scattered field of the single surface element of all surface elements.

[0105] Understandably, this approach integrates the principles of parameter discretization and normalization, spatial coordinate vector transformation, hierarchical database indexing and retrieval, and global grid traversal and adaptation to achieve batch solutions for the scattering field of surface elements in complex terrain scenarios. It uses a preset maximum feasible step size to discretize and round down the continuously changing actual frequency band parameters of the radar, aligning the numerical dimensions of the measured continuous operating parameters with the preset discrete frequency parameters in the electromagnetic characteristic database. It leverages a multi-attribute digital grid model to retrieve the physical surface information corresponding to each simulated surface element, combining the normalized rounded frequency band with surface attribute information to complete the first-level coarse search of the database, matching the initial scattering field file set corresponding to the surface type and frequency dimension. This achieves preliminary screening and range convergence of massive database files. Furthermore, relying on the spatial vector mapping of the coordinate transformation matrix and the coordinate system rotation transformation mechanism, it enables precise conversion of the radar incident wave vector from the standard global geodetic coordinate system to the local coordinate system corresponding to tilted and undulating terrain surface elements, correcting for the effects of natural terrain undulations and surface element normal vector offsets. The spatial deviation of the incident angle is calculated by decomposing and transforming the three-dimensional components of the incident wave vector to obtain the local incident elevation angle and local incident azimuth angle that fit the actual illumination state of the surface element. The local angle parameters are rounded and normalized by continuing the discrete calibration standard with the maximum feasible step size, so as to achieve accurate matching between the local angle parameters and the discrete angle sample parameters in the database. Based on the normalized angle parameters, the preliminary scattered field file set is subjected to a second-level fine screening to accurately locate the unique corresponding scattered field data file and extract the scattered field data of the standard single surface element. By traversing all terrain grid surface elements in the simulation area one by one, the standardized operation logic of parameter rounding, coordinate transformation, file screening and data extraction is uniformly reused to achieve batch, unified and accurate adaptation and retrieval of electromagnetic data of scattered surface elements of the entire terrain. The entire set of technical logic relies on multi-level parameter normalization and spatial coordinate correction mechanism to unify the parameter benchmark of the measured working conditions and database samples, adapt to the electromagnetic illumination characteristics of complex undulating terrain, and ensure the consistency, accuracy and standardization of the acquisition of scattered field data of the entire area.

[0106] For example, with a preset maximum feasible step size of 1.525 GHz for frequency and 1° for angle, and collecting actual radar operating parameters of 10.2 GHz for the actual frequency band, 22.3° for the global actual incident elevation angle, and 45.6° for the global actual incident azimuth angle, the 10.2 GHz actual frequency band is first discretized and rounded based on the maximum feasible step size of 1.525 GHz, resulting in a rounded frequency band of 9.525 GHz. This is then imported into a pre-constructed multi-attribute digital grid model, and the physical surface information of the current element to be calculated is read. Based on the dual search criteria of the 9.525GHz rounded frequency band and cement surface type, a primary search was performed on the multi-attribute composite surface electromagnetic feature database. This resulted in the selection of all scattered field data files with a frequency of 9.525GHz within the cement sub-database, forming a preliminary scattered field file set. This file set contains standardized scattered field data files with different incident angles and polarization types. A global-to-local spatial coordinate transformation matrix was then used to complete the vector mapping. This transformation matrix is ​​a three-dimensional orthogonal rotation matrix, and its parameters are determined by the spatial method of the current terrain surface element. The vector and terrain tilt angle are calculated to accurately map the radar incident wave vector beam information from the global geodetic coordinate system to the local coordinate system of the current tilted surface element. Angle calculations are performed on the transformed three-dimensional incident wave vector components to obtain a local incident elevation angle of 21° and a local incident azimuth angle of 46° in the local coordinate system. Based on a maximum feasible step size of 1°, these local angle parameters are rounded and calibrated to obtain rounded incident elevation angles of 21° and rounded incident azimuth angles of 46°. These rounded angle parameters are then used as search criteria to analyze the preliminary scattered field file set. A second round of precise filtering was performed to match and obtain scattering field data files of four polarization types (HH, VV, HV, VH) with corresponding angle parameters. The pre-stored standard complex scattering field values ​​in the files were read, and the scattering field data of the individual surface element corresponding to the current cement surface element was extracted. Following the same parameter rounding, coordinate transformation, angle calculation, file filtering, and data extraction process, iterative calculations were performed on all terrain grid surface elements in the multi-attribute digital grid model one by one, and finally, the batch acquisition of the scattering field data of the individual surface elements of all surface elements in the entire simulation area was completed.

[0107] Specifically, when rounding based on the maximum feasible step size, the following is included: The rounding formula is: ,in Here, x represents the actual operating parameters after rounding, and 'x' represents the actual operating parameters before rounding. This represents the maximum feasible step size corresponding to the current operating parameters.

[0108] Step 5: Determine the imaging range based on the radar illumination area, and divide the imaging range into several imaging resolution units based on the radar resolution; obtain the total scattering field of each imaging resolution unit, and obtain the RCS, one-dimensional range image and two-dimensional SAR image based on each total scattering field.

[0109] Specifically, obtaining the total scattering field of each of the imaging resolution units includes: Based on the coherent superposition method, the scattering fields of each individual surface element in the same imaging resolution unit are summed to obtain the total scattering field.

[0110] Understandably, the calculation of the total scattered field of surface echoes in SAR imaging scenarios is based on the principle of coherent superposition of electromagnetic waves and the principle of energy aggregation of imaging resolution units. Electromagnetic waves possess wave coherence characteristics, and the scattered electromagnetic waves generated by all surface elements within the same imaging resolution unit are coherent wave sources with the same frequency, source, and phase correlation, satisfying the physical conditions for coherent superposition. As the smallest independent pixel resolution unit in the radar imaging system, the imaging resolution unit has a fixed spatial size and electromagnetic integration range. A single resolution unit contains several geometrically independent, finely detailed terrain surface elements with different scattering responses. The scattered field of each surface element carries independent scattering amplitude and scattering phase information. The coherent superposition method follows the linear superposition operation rules of wave optics and can calculate the complex form of the scattered field of all surface elements within the same imaging resolution unit. Unlike simple scalar numerical superposition, point-by-point vector accumulation operations can completely preserve the phase difference and amplitude difference of electromagnetic waves scattered by each surface element, realizing the vector synthesis of multi-source scattered electromagnetic waves. This operation mode closely matches the physical process of actual radar electromagnetic wave scattering, propagation, and echo coupling, accurately reproducing the mutual interference, superposition, and coupling state of scattered signals from multiple fine surface elements within the same imaging resolution unit. By integrating the global summation of the scattered fields of all surface elements within the unit, the microscopic scattering response of the fine grid surface elements can be aggregated into the macroscopic resolution unit scattering signal that can be identified by radar imaging. This completes the scale conversion from microscopic surface element scattering data to macroscopic imaging pixel scattering data, ensuring that the final total scattering field can truly characterize the overall electromagnetic scattering characteristics of the corresponding imaging resolution unit. It is fully compatible with the pixel-level imaging calculation logic and electromagnetic simulation mechanism of radar images.

[0111] Specifically, when obtaining the RCS, one-dimensional range profile, and two-dimensional SAR image based on the total scattering fields, the process includes: The total scattered fields are superimposed, and the square of the superposition result is taken to obtain the RCS of the entire region where the target is located. The total scattered fields are merged along the azimuth direction, and the merged result is stored in a one-dimensional array to obtain the one-dimensional range image; The total scattered fields are stored in a two-dimensional array according to their spatial location to obtain the two-dimensional SAR image.

[0112] Understandably, relying on the principle of numerical solution of radar cross section, the principle of one-dimensional range image dimension mapping, and the principle of two-dimensional SAR image spatial reconstruction, the synchronous calculation and imaging reconstruction of multiple types of radar echo characteristic parameters are achieved. The physical definition of radar cross section is an effective characterization of the energy of the scattered echo from the target area. Following the rules of electromagnetic energy superposition and amplitude mapping, the total scattered field of all imaging resolution units can form the overall synthetic scattered field of the target after global vector superposition. Performing modulo squaring operation on the synthetic scattered field result can convert the complex vector scattered signal into a scalar value characterizing the intensity of the echo energy. This aligns with the core computing mechanism of RCS based on quantifying the target's scattering capability using the total scattered energy. The one-dimensional range image relies on the principle of echo sequence arrangement in the radar range dimension. Taking the range direction position of radar electromagnetic wave propagation as the arrangement benchmark, it performs dimensional fusion and orderly merging of the total scattered field of imaging resolution units in different azimuth directions and the same range dimension, thus combining the continuous range... The scattered echo signal is neatly organized into a one-dimensional linear array structure to achieve serialized mapping of the scattering characteristics in the range dimension, restoring the scattering intensity distribution characteristics of the target along the radar line of sight. The two-dimensional SAR image follows the principle of geospatial grid mapping and pixel matrix arrangement, strictly matching the real spatial coordinate position of the surface imaging resolution unit. The total scattered field data corresponding to each resolution unit is filled into the row and column matrix nodes of the two-dimensional array one by one. Based on the spatial topological correspondence of the two-dimensional array, the horizontal and vertical spatial distribution characteristics of the surface area are reproduced, and the scattering intensity differences and spatial correlation characteristics of different locations are fully preserved. The entire technical process realizes the standardized transformation of the original electromagnetic scattering data into RCS parameters, one-dimensional range feature sequences, and two-dimensional spatial imaging matrices through hierarchical operations of complex scattered field energy conversion, single-dimensional sequence reconstruction, and two-dimensional matrix mapping, adapting to the underlying data operation logic of radar target feature analysis and imaging simulation.

[0113] For example, the simulation area contains 200 imaging resolution units, each corresponding to a set of complex total scattered field data. First, a global vector superposition operation is performed on the total scattered field of all imaging resolution units to obtain the overall composite scattered field of the region. The RCS is then calculated using the formula σ=lim[4πR 2 •|E_total| 2 The solution is complete, where σ is the radar cross section of the target area, R is the simulated distance between the radar and the center of the simulated area (taken as 1000m), E_total is the overall composite scattered field after the superposition of all total scattered field vectors, and |・| 2For complex modulus-square operations, the composite scattered field values ​​after global superposition are substituted into the formula to complete the modulus-square operation and energy conversion, obtaining the RCS numerical results of the entire surface area to be measured. Next, using the radar range direction as the vertical reference and the azimuth direction as the horizontal reference, all total scattered fields corresponding to different azimuth positions within the same range threshold are dimensionally merged and sorted, and then sequentially stored in a one-dimensional storage array of dimension 1×200 according to the radar detection range from near to far, completing the construction of a one-dimensional range profile of the target area. Figure 4 As shown; each index node of the one-dimensional array corresponds to the scattering intensity characteristics at different distances. Finally, based on the horizontal and vertical topological positions of each imaging resolution unit in geographic space, all total scattering field data are sequentially filled into a two-dimensional grid array of size 20×10. The row dimension of the two-dimensional array corresponds to the spatial position in the geographic orientation direction, and the column dimension corresponds to the spatial position in the geographic distance direction. The values ​​of each matrix node within the array correspond one-to-one with the scattering field characteristics of the actual surface imaging resolution unit. This completes the simulation construction of a two-dimensional SAR image containing complete spatial scattering information, as shown. Figure 5 As shown.

[0114] In the above embodiments, by pre-setting the non-polarization parameter range and polarization type set, the maximum feasible step size is determined by the bisection iteration method and the combination of working condition parameters is generated. This completes the pre-calculation of the scattering field of a single surface element and the construction of a multi-attribute composite surface electromagnetic feature database. At the same time, by combining the DEM digital elevation image and the composite surface attribute partitioning map, grid alignment, clipping, segmentation and grid partitioning are realized to quickly generate a multi-attribute surface digital grid model. Then, the scattering field of each surface element is obtained by coordinate transformation and parameter rounding lookup table. The total scattering field of the imaging resolution unit is obtained by coherent superposition. Finally, the RCS, one-dimensional range image and two-dimensional SAR image are output. The overall method can significantly reduce the real-time calculation of electromagnetic scattering of complex surfaces and greatly improve the calculation efficiency. At the same time, it takes into account the calculation accuracy under multiple types of surfaces and multiple working condition parameters. It effectively solves the problems of complex modeling, slow calculation and insufficient accuracy of traditional methods in complex undulating surfaces and multi-attribute composite scenes. It can quickly, stably and accurately generate multi-attribute composite surface electromagnetic characteristics and meet the needs of efficient and high-precision electromagnetic simulation for practical engineering applications such as radar detection, geomorphological exploration and target recognition.

[0115] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0116] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0117] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0118] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for rapidly generating multi-attribute composite surface electromagnetic properties, characterized in that, include: A set of non-polarization parameter ranges and polarization types for radar electromagnetic waves is preset, and the maximum feasible step size for each non-polarization parameter range is obtained based on the binary iterative method; independent combinations of operating parameters are obtained based on each maximum feasible step size. A set of predefined land surface types is established, and numerical calculations of the scattering field of a single surface element are performed on each land surface type in the set of land surface types based on the combination of operating parameters. A multi-attribute composite land surface electromagnetic feature database is constructed based on the land surface types, the combination of operating parameters, and the scattering field of the single surface element. Obtain a DEM digital elevation image and a composite surface attribute map of the entire area where the target is located; perform grid alignment and cropping on the DEM digital elevation image and the composite surface attribute map; perform interpolation and densification on the DEM digital elevation image; obtain a multi-attribute digital grid model of the surface based on the segmentation and grid partitioning of the DEM digital elevation image and the composite surface attribute map; The actual operating parameters of the radar are obtained, and the actual operating parameters are transformed into a local coordinate system based on the coordinate transformation matrix to obtain the local operating parameters of the current surface element; the scattering field of the single surface element corresponding to the current surface element is obtained based on the local operating parameters; the scattering field of the single surface element of all surface elements is obtained by traversing all surface elements. The imaging range is determined based on the radar illumination area, and the imaging range is divided into several imaging resolution units based on the radar resolution; the total scattering field of each imaging resolution unit is obtained, and the RCS, one-dimensional range image and two-dimensional SAR image are obtained based on each total scattering field.

2. The method for rapidly generating multi-attribute composite surface electromagnetic properties according to claim 1, characterized in that, When obtaining independent combinations of operating parameters based on each of the maximum feasible step sizes, the following are included: Based on the maximum feasible step size, each of the non-polarization parameter intervals is discretely sampled, and a set of non-polarization parameters corresponding to each of the non-polarization parameter intervals is constructed based on the collected non-polarization parameters. By combining the non-polarization parameters in each set of non-polarization parameters with the polarization types in each set of polarization types, several sets of independent operating condition parameter combinations are obtained.

3. The method for rapidly generating multi-attribute composite surface electromagnetic properties according to claim 2, characterized in that, When constructing a multi-attribute composite surface electromagnetic feature database based on the surface type, the combination of operating parameters, and the scattering field of a single surface element, the following is included: The multi-attribute composite surface electromagnetic feature database is divided into a corresponding number of electromagnetic feature sub-databases based on the number of surface types, and the electromagnetic feature sub-databases are named based on the surface types. For each set of operating condition parameter combinations, a scattering field data file is created in each of the electromagnetic feature sub-libraries; The scattered field data file is named based on the combination of operating parameters, and the scattered field of a single surface element is stored in the corresponding scattered field data file based on the surface type and the combination of operating parameters. Using the surface type as the primary index and the combination of operating parameters as the secondary index, a joint index marker is established for the scattering field of each individual surface element in the multi-attribute composite surface electromagnetic feature database based on the primary index and the secondary index.

4. The method for rapidly generating multi-attribute composite surface electromagnetic properties according to claim 3, characterized in that, When performing grid alignment and cropping on the DEM digital elevation image and the composite surface attribute map, the following steps are included: The DEM digital elevation image includes pixel unit elevation values ​​and geographic reference information; the latitude and longitude coordinates of the pixel units of the DEM digital elevation image are obtained based on the geographic reference information, and the land surface type is divided into the whole area based on the RGB colors in the composite land surface attribute division map; Obtain the latitude and longitude coordinates of the corner points of the composite surface attribute division map, and perform grid alignment between the composite surface attribute division map and the DEM digital elevation image based on the latitude and longitude coordinates of the corner points and the latitude and longitude coordinates of the pixel units; The portion of the DEM digital elevation image that extends beyond the composite surface attribute delineation map is cropped.

5. The method for rapidly generating multi-attribute composite surface electromagnetic properties according to claim 4, characterized in that, When obtaining a multi-attribute digital grid model of the surface based on segmentation and meshing of the DEM digital elevation image and the composite surface attribute partitioning map, the following steps are included: The DEM digital elevation image and the composite surface attribute map are divided into uniform blocks, and each block is labeled with a block number. Based on the center point coordinates, range dimension, and azimuth dimension of the radar illumination area, obtain the vertex coordinates of the four vertices of the radar illumination area, and obtain the maximum and minimum values ​​among the vertex coordinates; based on the maximum and minimum values, determine the block covered by the radar illumination area, and obtain the block number of the covered block.

6. The method for rapidly generating multi-attribute composite surface electromagnetic properties according to claim 5, characterized in that, When obtaining a multi-attribute digital grid model of the surface based on segmentation and meshing of the DEM digital elevation image and the composite surface attribute partitioning map, the method further includes: Based on the block number, the corresponding blocks are extracted from the DEM digital elevation image and the composite surface attribute division map, and the extracted blocks are read and merged to obtain the extracted elevation image and the extracted attribute division map. Redundancy is removed from the extracted elevation image and the extracted attribute partitioning map, and the extracted elevation image and the extracted attribute partitioning map are then meshed. Obtain the grid coordinates of each grid point in the grid, and obtain the pixel unit elevation value and physical surface information of the surface element corresponding to the grid coordinates based on the nearest neighbor interpolation method; The pixel unit elevation value and the physical surface information are stored in a two-dimensional array to obtain the multi-attribute digital grid model of the land surface.

7. The method for rapidly generating multi-attribute composite surface electromagnetic properties according to claim 6, characterized in that, The actual operating parameters of the radar are obtained, and the actual operating parameters are transformed into a local coordinate system based on the coordinate transformation matrix to obtain the local operating parameters of the current surface element; the scattering field of the single surface element corresponding to the current surface element is obtained based on the local operating parameters. When traversing all surface elements to obtain the individual surface element scattering field of all surface elements, the process includes: Based on the maximum feasible step size, the actual frequency band in the actual working condition parameters is rounded to obtain the rounded frequency band; the multi-attribute digital grid model of the land surface is imported, and the physical surface information is obtained; based on the rounded frequency band and the physical surface information, the multi-attribute composite surface electromagnetic feature database is queried to obtain a preliminary scattered field file set; Based on the coordinate transformation matrix, the beam information corresponding to the incident wave vector actually emitted by the radar is mapped from the global geodetic coordinate system to the local coordinate system of the current surface element; based on the transformed incident wave vector components, the local incident elevation angle and local incident azimuth angle in the local coordinate system are obtained from the actual incident elevation angle and the actual incident azimuth angle in the actual operating condition parameters.

8. The method for rapidly generating multi-attribute composite surface electromagnetic properties according to claim 7, characterized in that, The actual operating parameters of the radar are obtained, and the actual operating parameters are transformed into a local coordinate system based on the coordinate transformation matrix to obtain the local operating parameters of the current surface element; the scattering field of the single surface element corresponding to the current surface element is obtained based on the local operating parameters. When traversing all surface elements to obtain the individual surface element scattering field of all surface elements, the process also includes: Based on the maximum feasible step size, the local incident elevation angle and the local incident azimuth angle are rounded to obtain the rounded incident elevation angle and the rounded incident azimuth angle. Based on the rounded incident elevation angle and the rounded incident azimuth angle, the corresponding scattered field data file is selected from the preliminary scattered field file set, and the scattered field of the single surface element corresponding to the current surface element is extracted from the selected scattered field data file; Traverse all surface elements and repeat coordinate transformation, rounding, filtering and extraction operations to obtain the individual surface element scattering field of all surface elements.

9. The method for rapidly generating multi-attribute composite surface electromagnetic properties according to claim 8, characterized in that, When obtaining the total scattering field of each of the imaging resolution units, the following steps are included: Based on the coherent superposition method, the scattering fields of each individual surface element in the same imaging resolution unit are summed to obtain the total scattering field.

10. The method for rapidly generating multi-attribute composite surface electromagnetic properties according to claim 9, characterized in that, When obtaining the RCS, one-dimensional range profile, and two-dimensional SAR image based on the total scattering field, the following are included: The total scattered fields are superimposed, and the modulus of the superposition result is squared to obtain the RCS of the entire region where the target is located. The total scattered fields are merged along the azimuth direction, and the merged result is stored in a one-dimensional array to obtain the one-dimensional range image; The total scattered fields are stored in a two-dimensional array according to their spatial location to obtain the two-dimensional SAR image.

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