Method and device for quantitatively evaluating electromagnetic propagation influence in inhomogeneous atmospheric waveguide environment

Abstract

The application provides a method and device for quantitatively evaluating electromagnetic propagation influence in a non-uniform atmospheric waveguide environment, and belongs to the field of ocean electromagnetic propagation and atmospheric waveguide detection. The method comprises the following steps: obtaining meteorological data of a research area; inversely deriving waveguide parameters corresponding to each spatial grid point in the research area according to the meteorological data, and constructing a spatial distribution of the waveguide parameters; calculating a waveguide similarity index corresponding to a target grid point in the ocean area; traversing each ocean grid point in the research area to obtain a spatial distribution of the waveguide similarity index; determining a propagation loss deviation corresponding to the research area; establishing a corresponding relationship between the waveguide similarity index and the propagation loss deviation; and determining a propagation loss deviation corresponding to a target spatial position according to a waveguide similarity index corresponding to the target spatial position and the corresponding relationship. The method and device provided by the application can reduce the calculation cost of evaluating the electromagnetic propagation influence in a non-uniform waveguide environment and eliminate the dependence on complete path profile data.

Description

Technical Field

[0001] This application relates to the field of marine electromagnetic propagation and atmospheric waveguide detection technology, and in particular to a method and apparatus for quantitatively assessing the impact of electromagnetic propagation in a non-uniform atmospheric waveguide environment. Background Technology

[0002] Atmospheric waveguides are a special propagation environment formed by abnormal distributions of atmospheric refractive index. They can significantly alter the propagation path of electromagnetic waves, causing them to propagate beyond line of sight near the sea surface or creating radar blind spots. The propagation performance of maritime communication, radar detection, and electronic countermeasures systems is closely related to the atmospheric waveguide environment.

[0003] Existing research primarily focuses on propagation analysis in horizontally homogeneous atmospheric waveguide environments, studying the propagation characteristics of electromagnetic waves through idealized waveguide models. For example, evaporation waveguide diagnostic models are constructed based on the Monin-Obukhov similarity theory, and the evaporation waveguide height or atmospheric corrected refractive index profile is inverted using meteorological data. Then, numerical methods such as parabolic equations are used to solve for the propagation loss under a given waveguide environment. These methods typically assume that the waveguide parameters are constant in the horizontal direction.

[0004] However, waveguide parameters in real marine environments (such as evaporation waveguide height) typically vary with spatial location, exhibiting significant horizontal inhomogeneity. As electromagnetic waves propagate along the propagation path, changes in waveguide height and structure cause the waves to gradually detach from the waveguide layer, resulting in a significant discrepancy between actual propagation loss and predictions based on the assumption of horizontal uniformity.

[0005] Currently, research on the impact of non-uniform waveguide environments largely focuses on specific propagation paths, lacking a unified index to quantitatively describe horizontal non-uniformity in waveguides and its impact on propagation loss. Existing techniques have proposed methods for predicting atmospheric modified refractive index profiles in non-uniform waveguide environments, but these lack analysis of their impact on electromagnetic propagation loss. Other techniques have proposed methods for predicting electromagnetic propagation loss in non-uniform waveguide environments, but these rely on solving the wave equation point-by-point along the propagation path, resulting in high computational costs and inability to implement them when complete path profile data is unavailable. Therefore, there is an urgent need for an evaluation scheme that can reduce computational costs and eliminate dependence on path profile data. Summary of the Invention

[0006] In view of this, this application provides a method and apparatus for quantitatively assessing the impact of electromagnetic propagation in a non-uniform atmospheric waveguide environment, in order to solve the problem that the existing technology lacks an index for quantitatively characterizing the horizontal non-uniformity of atmospheric waveguides, which leads to the problem that the assessment of the impact of electromagnetic propagation in a non-uniform environment requires solving the wave equation point by point along the propagation path, resulting in high computational costs and dependence on complete path profile data.

[0007] Specifically, this application is implemented through the following technical solution:

[0008] The first aspect of this application provides a method for quantitatively assessing the impact of electromagnetic propagation in a non-uniform atmospheric waveguide environment, the method comprising:

[0009] Obtain meteorological data for the study area;

[0010] Based on the meteorological data, the waveguide parameters corresponding to each spatial grid point in the study area are obtained by inversion, and the spatial distribution of waveguide parameters is constructed.

[0011] For a target grid point in the ocean region, a spatial neighborhood is constructed with the target grid point as the center. Waveguide parameters in the spatial neighborhood are obtained along multiple spatial directions. Based on the differences between the waveguide parameters in different spatial directions and the waveguide parameters of the target grid point, as well as the spatial distance between the target grid point and the sampling points in the neighborhood, the waveguide similarity index corresponding to the target grid point is calculated.

[0012] By traversing all ocean grid points within the study area, the spatial distribution of waveguide similarity index was obtained;

[0013] Determine the propagation loss deviation corresponding to the study area. The propagation loss deviation is used to characterize the change in propagation loss caused by the horizontal non-uniform waveguide environment relative to the horizontal uniform waveguide environment.

[0014] Establish the correspondence between the waveguide similarity index and the propagation loss deviation;

[0015] Based on the waveguide similarity index corresponding to the target spatial location and the corresponding relationship, the propagation loss deviation corresponding to the target spatial location is determined.

[0016] The second aspect of this application provides a device for quantitatively assessing the impact of electromagnetic propagation in a non-uniform atmospheric waveguide environment. The device includes an acquisition module, a construction module, a calculation module, a traversal module, a determination module, and an establishment module.

[0017] The acquisition module is used to acquire meteorological data of the study area;

[0018] The construction module is used to invert the meteorological data to obtain the waveguide parameters corresponding to each spatial grid point in the study area, and to construct the spatial distribution of waveguide parameters.

[0019] The calculation module is used to construct a spatial neighborhood centered on the target grid point in the ocean area, obtain waveguide parameters in the spatial neighborhood along multiple spatial directions, and calculate the waveguide similarity index corresponding to the target grid point based on the differences between the waveguide parameters in different spatial directions and the waveguide parameters of the target grid point, as well as the spatial distance between the target grid point and the sampling points in the neighborhood.

[0020] The traversal module is used to traverse each ocean grid point within the study area to obtain the spatial distribution of the waveguide similarity index;

[0021] The determining module is used to determine the propagation loss deviation corresponding to the study area. The propagation loss deviation is used to characterize the change in propagation loss caused by the horizontal non-uniform waveguide environment relative to the horizontal uniform waveguide environment.

[0022] The establishment module is used to establish the correspondence between the waveguide similarity index and the propagation loss deviation;

[0023] The determining module is further configured to determine the propagation loss deviation corresponding to the target spatial location based on the waveguide similarity index corresponding to the target spatial location and the corresponding relationship.

[0024] The method and apparatus for quantitatively assessing the impact of electromagnetic propagation in a non-uniform atmospheric waveguide environment provided in this application, by constructing a waveguide similarity index and establishing its correspondence with propagation loss deviation, can quickly assess the impact of a non-uniform waveguide environment on electromagnetic propagation without solving the wave equation point by point along the propagation path. This significantly reduces computational costs and eliminates dependence on complete path profile data. Specifically, the spatial distribution of waveguide parameters is obtained based on meteorological data inversion, providing basic data support for subsequent quantification. For marine target grid points, waveguide parameters in the neighborhood are obtained along multiple spatial directions, and parameter differences and spatial distances are considered simultaneously to construct a waveguide similarity index that can comprehensively reflect the horizontal non-uniformity of the waveguide. The spatial distribution of this index is obtained by traversing the study area, which can intuitively show the degree of non-uniformity in different regions. Propagation loss deviation is defined as a quantitative indicator to measure the impact of non-uniformity, and its correspondence with the waveguide similarity index is established. This allows for the rapid determination of the corresponding propagation loss deviation for any target spatial location by simply calculating its waveguide similarity index, thus achieving a quantitative assessment of the impact on electromagnetic propagation. Attached Figure Description

[0025] Figure 1 A flowchart of Example 1 of the method for quantitatively assessing the impact of electromagnetic propagation in a non-uniform atmospheric waveguide environment provided in this application;

[0026] Figure 2 The correlation analysis results of waveguide similarity index and propagation loss deviation shown in this embodiment are as follows;

[0027] Figure 3 This is a graph showing the relationship between the calculated propagation loss deviation and the propagation distance, as illustrated in this embodiment.

[0028] Figure 4 This is a schematic diagram of the second embodiment of the device for quantitatively evaluating the impact of electromagnetic propagation in a non-uniform atmospheric waveguide environment provided in this application. Detailed Implementation

[0029] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0030] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used herein are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0031] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0032] Example 1

[0033] The following specific embodiments are given to illustrate the technical solution of this application in detail.

[0034] Figure 1 This is a flowchart of Example 1 of the method for quantitatively assessing the impact of electromagnetic propagation in a non-uniform atmospheric waveguide environment provided in this application. Please refer to... Figure 1 The method provided in this embodiment may include:

[0035] S101. Obtain meteorological data for the study area.

[0036] It should be noted that the study area refers to the marine area where the impact of electromagnetic propagation needs to be assessed. Meteorological data is the basic data source for subsequent inversion of atmospheric waveguide parameters, and its quality and spatiotemporal resolution directly affect the accuracy of the spatial distribution of waveguide parameters.

[0037] The meteorological data acquired in this step specifically includes air temperature data, relative humidity data, air pressure data, sea surface temperature data, and wind speed data. Among these, air temperature and air pressure affect atmospheric density and evaporation flux, relative humidity determines water vapor content, while sea surface temperature and wind speed control turbulent exchange at the air-sea interface. These environmental parameters are the main factors affecting the formation and intensity of evaporation waveguides.

[0038] To balance data accuracy and computational efficiency, this embodiment preferably uses ERA5 reanalysis data provided by ECMWF and CFSv2 reanalysis data provided by NCEP. Reanalysis data are meteorological datasets generated by fusing historical observation data with numerical weather prediction models, offering advantages such as strong spatiotemporal continuity and wide coverage. Specifically, the meteorological data has a temporal resolution of 1 hour and a spatial resolution of 0.25° × 0.25°, corresponding to a ground distance of approximately 25 km × 25 km. This resolution effectively captures the spatial distribution characteristics of atmospheric waveguide parameters in the study area, such as the evaporation duct height, including gradient changes between nearshore and offshore areas, while controlling the amount of data required for subsequent numerical calculations to avoid excessive computational burden due to overly dense data.

[0039] It should be noted that the data sources and resolutions mentioned above are exemplary choices. Other meteorological reanalysis data, such as JRA-55 or MERRA-2 data, can be selected based on the scale of the actual study area, computational resources, and accuracy requirements. Field observation data can also be used, as long as sufficient spatiotemporal resolution of the meteorological element field can be obtained. For example, for small-scale, detailed assessments, higher resolution data, such as 0.125°×0.125°, can be used; for large-scale, rapid assessments, lower resolution can be used to reduce computational costs.

[0040] S102. Based on the meteorological data, the waveguide parameters corresponding to each spatial grid point in the study area are obtained by inversion, and the spatial distribution of waveguide parameters is constructed.

[0041] It should be noted that in the marine atmospheric environment, evaporation waveguides are the most common type of atmospheric waveguide and have the most significant impact on electromagnetic propagation. The formation mechanism of evaporation waveguides is as follows: seawater evaporation causes the water vapor content in the near-sea atmosphere to decrease with altitude, while atmospheric temperature also varies with altitude. Both of these factors together result in a negative gradient of the atmospheric corrected refractive index in the vertical direction, thus forming a waveguide layer. The key parameter characterizing evaporation waveguides is the evaporation waveguide height, which is the thickness of the waveguide layer measured from the sea surface. In this step, the waveguide parameter specifically refers to the evaporation waveguide height.

[0042] Specifically, the step of retrieving waveguide parameters corresponding to each spatial grid point within the study area based on the meteorological data includes:

[0043] (1) Construct an evaporation waveguide diagnostic model based on Monin-Obukhov similarity theory.

[0044] The Monin-Obukhov similarity theory is a classic theory describing near-surface atmospheric turbulent exchange. This theory establishes a dimensionless relationship function between the vertical profiles of average quantities and turbulent fluxes in the near-sea atmosphere by comprehensively considering the similarity relationships among momentum flux, sensible heat flux, and latent heat flux at the air-sea interface. Based on this theory, an evaporation waveguide diagnostic model can be constructed to quantitatively correlate conventional meteorological observation data (such as wind speed, air temperature, sea surface temperature, and humidity) with the vertical profile of the atmospheric corrected refractive index, thereby diagnosing the structural parameters of the evaporation waveguide. Commonly used evaporation waveguide diagnostic models include the PJ model, the Babin model, and the NPS model; this embodiment does not limit the specific model form.

[0045] (2) The meteorological data is processed using the evaporation waveguide diagnostic model to obtain the evaporation waveguide height corresponding to each spatial grid point.

[0046] The meteorological data obtained in step S101 at each spatial grid point are input into the constructed evaporation waveguide diagnostic model. The model first calculates intermediate parameters such as the frictional velocity at the air-sea interface, the Monin-Obukhov length, the sensible heat flux, and the latent heat flux, and then solves for the near-sea surface atmospheric corrected refractive index profile. By identifying the location of the minimum value in the atmospheric corrected refractive index profile, the waveguide layer thickness of the evaporation waveguide, i.e., the evaporation waveguide height, can be determined. For each spatial grid point in the study area, the above calculation is performed point by point to obtain the corresponding evaporation waveguide height value for each grid point.

[0047] (3) The height of the evaporation waveguide is used as the waveguide parameter of the corresponding spatial grid point.

[0048] The calculated evaporation waveguide heights are assigned to corresponding spatial grid points, thus transforming discrete grid data into a continuous spatial distribution field of waveguide parameters. Furthermore, different color depths can represent variations in evaporation waveguide heights; the heights are relatively low in nearshore areas, while gradually increasing in offshore areas. This creates a spatial distribution map that visually demonstrates the horizontal non-uniformity of evaporation waveguide heights, providing a foundational parameter field for subsequent calculations of the waveguide similarity index.

[0049] It should be noted that the evaporation waveguide height is only one preferred waveguide parameter. For different waveguide types, other characteristic parameters in the atmospheric modified refractive index profile, such as waveguide intensity, waveguide layer thickness, and trapping layer thickness, can also be used as waveguide parameters for spatial grid points, as long as the parameter can reflect the spatial variation characteristics of the waveguide structure.

[0050] S103. For a target grid point in the ocean area, construct a spatial neighborhood with the target grid point as the center, obtain waveguide parameters in the spatial neighborhood along multiple spatial directions, and calculate the waveguide similarity index corresponding to the target grid point based on the differences between the waveguide parameters in different spatial directions and the waveguide parameters of the target grid point, as well as the spatial distance between the target grid point and the sampling points in the neighborhood.

[0051] It should be noted that this step mainly constructs the Duct Similarity Index (DSI), a dimensionless index used to quantitatively characterize the degree of non-uniformity of waveguide parameters in the horizontal direction within the neighborhood of a target grid point. A higher DSI value indicates more uniform waveguide parameters in the neighborhood (strong spatial continuity); a lower DSI value indicates more drastic spatial variation of waveguide parameters (significant horizontal non-uniformity).

[0052] It should be noted that for any target grid point belonging to the ocean region (denoted as position coordinates) First, a sampling area needs to be established around it and the waveguide parameters of each sampling point need to be obtained.

[0053] Specifically, the step of constructing a spatial neighborhood centered on the target grid point and obtaining waveguide parameters within the spatial neighborhood along multiple spatial directions includes:

[0054] (1) Establish a spatial neighborhood with the target grid point as the center within a preset range.

[0055] A spatial neighborhood refers to the region spatially adjacent to the target grid point. The size of the neighborhood can be set according to actual application requirements, such as a circular region with a radius of several grid squares centered on the target grid point, or a square region with a side length of several grid squares. In this embodiment, the neighborhood is set to cover the area of ​​several surrounding spatial grid points, and the specific radius can be determined according to the spatial variation scale of the waveguide parameters.

[0056] (2) Obtain neighborhood sampling points within the spatial neighborhood along multiple preset spatial directions.

[0057] Spatial direction refers to the direction of a ray extending outward along a horizontal plane from the target grid point. The number of spatial directions... The angle between the calculated accuracy and efficiency can be determined, for example, by taking 8 directions: the angle with the due east direction. The angles are 0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°. In each spatial direction, multiple sampling points are sequentially acquired by extending outwards from the target grid point. Neighborhood sampling points refer to discrete spatial grid points selected along each spatial direction within the spatial neighborhood, such as positions 1, 2, or 3 times the grid distance from the target grid point.

[0058] (3) Obtain the waveguide parameters corresponding to each neighborhood sampling point.

[0059] That is, the evaporation waveguide height obtained in step S102, and the waveguide parameters of the target grid point itself are denoted as... , No. The first spatial direction The waveguide parameters at each sampling point are: .

[0060] After obtaining the waveguide parameters at each sampling point, it is necessary to quantify the similarity of the waveguide parameters between each sampling point and the target grid point. Specifically, this includes:

[0061] (1) For each spatial direction, calculate the parameter difference between the waveguide parameters corresponding to each neighboring sampling point in the current spatial direction and the waveguide parameters corresponding to the target grid point.

[0062] It should be noted that for each spatial direction Iterate through all neighboring sampling points along this direction and calculate the difference. .

[0063] (2) Determine the similarity value of the corresponding neighborhood sampling points based on the difference of each parameter.

[0064] In this embodiment, the similarity function adopts the exponential decay function, the expression of which is:

[0065] ;

[0066] in, This is a similarity scaling parameter used to control the rate at which similarity decreases as the parameter difference increases. The value of can be set according to the typical variation range of waveguide parameters, for example, it can be taken as the mean square error of the evaporation waveguide height or an empirical constant. This exponential decay function can map the parameter difference to Within the interval: the smaller the difference, the closer the similarity value is to 1; the larger the difference, the closer the similarity value is to 0. It should be noted that the above similarity function form is only an example, and other function forms can also be used, as long as they can reflect the negative correlation between parameter differences and similarity.

[0067] After obtaining the similarity values ​​of each sampling point, it is necessary to further consider the spatial distance between the sampling point and the target grid point; sampling points that are closer should have higher weights. Specifically, this includes:

[0068] (1) For each spatial direction, calculate the spatial distance between each neighboring sampling point and the target grid point in the current spatial direction.

[0069] It should be noted that spatial distance Euclidean distance can be used, which is calculated based on the difference in latitude and longitude between the sampling point and the target grid point.

[0070] (2) Determine the corresponding distance weight based on the spatial distance between each neighboring sampling point and the target grid point.

[0071] The distance weight decreases as the distance between the sampling point and the target grid point increases, making the contribution of the neighboring region to the waveguide similarity index higher than that of the distant region. The distance weight function can take various forms, such as:

[0072] or ;

[0073] in, Indicates distance weight, This represents the spatial distance between the i-th neighboring sampling point and the target grid point. The radius of the spatial neighborhood. The attenuation length parameter (a positive real number) can be determined based on the spatial correlation scale of the waveguide parameters. This embodiment does not limit the specific function form.

[0074] (3) The similarity values ​​of each neighboring sampling point in the current spatial direction are weighted using the distance weight to obtain the directional similarity index corresponding to the current spatial direction.

[0075] It should be noted that the directional similarity index is used to quantify the overall similarity of waveguide parameters in a single spatial direction. The calculation formula is:

[0076] ;

[0077] The numerator is the sum of the products of the similarity values ​​of each sampling point and the distance weights, and the denominator is the sum of the distance weights, thus achieving a normalized weighted average. For similarity function, For distance weighting function, For grid point positions, The angle between the sampling direction and the due east direction.

[0078] (4) The directional similarity indices corresponding to multiple spatial directions are averaged to obtain the waveguide similarity index corresponding to the target grid point.

[0079] Specifically, the calculation formula is as follows:

[0080] ;

[0081] That is, by taking the arithmetic mean of the directional similarity indices in all directions, the final DSI is a dimensionless exponent between 0 and 1. A higher value indicates more uniform waveguide parameters within the neighborhood of the target grid point, while a lower value indicates more drastic spatial variations in waveguide parameters. This represents the number of spatial directions. For other parameters, please refer to the previous introduction, which will not be repeated here.

[0082] Specifically, the spatial distribution of the waveguide similarity index (DSI) in a certain sea area was calculated, with different color depths representing the magnitude of the DSI values. The DSI values ​​in nearshore areas are lower (lighter color), indicating drastic variations in waveguide parameters in these areas; while the DSI values ​​in offshore areas are higher (darker color), indicating relatively uniform waveguide parameters. This distribution pattern is consistent with the spatial distribution characteristics of evaporation waveguide height shown in the resulting spatial distribution map, verifying the effectiveness of the DSI index.

[0083] As an optional optimization method in this embodiment, before starting the DSI calculation, ocean grid points in the study area can be pre-identified, thereby skipping land grid points to improve computational efficiency. Specifically, land-sea mask data corresponding to the study area can be obtained. A land-sea mask is a binary data set that identifies whether each spatial grid point belongs to land or ocean, and can be obtained from a global coastline database or geographic information system data. Based on this land-sea mask data, spatial grid points corresponding to ocean areas are identified. For each spatial grid point, its mask value is queried. If the mask value indicates ocean, it is retained; if it indicates land, it is excluded. The identified ocean area spatial grid points are then determined as the objects for waveguide similarity index calculation, while land grid points are skipped without further calculation. It should be noted that this identification step is not a necessary prerequisite for calculating the DSI. Other methods can also be used to determine whether a target grid point belongs to an ocean area, such as directly receiving the ocean grid point range specified by the user, or judging based on known coastline coordinates.

[0084] It should be noted that the spatial variation characteristics of evaporation waveguide parameters differ significantly across different sea areas and seasons. Fixed neighborhood range, number of directions, and similarity scale parameters may reduce the ability of DSI to characterize inhomogeneities at different scales. Therefore, in this embodiment, before calculating DSI, the following calculation parameters are dynamically determined based on the spatial distribution of waveguide parameters obtained in step S102.

[0085] Regarding the radius of the spatial neighborhood, the spatial variability function of the waveguide parameters within the study area is calculated to obtain the range value of the degree of variability as a function of distance. For example, the neighborhood radius is set to 0.5 to 1 times this range value to ensure that the neighborhood range covers the main spatially relevant scales. When the spatial variability of the waveguide parameters is drastic, a smaller neighborhood radius is used to preserve local details; when the spatial variability of the waveguide parameters is gradual, a larger neighborhood radius is used to obtain stable statistical characteristics.

[0086] Regarding the number and specific orientation of spatial directions, the anisotropy index of the spatial distribution of waveguide parameters is calculated, which is the difference in variability function in different directions. If the anisotropy index is lower than a preset threshold, it indicates that the waveguide parameters vary uniformly in different directions. In this case, fewer spatial directions are used, such as four directions: 0°, 90°, 180°, and 270°, which is sufficient to characterize the waveguide parameters. If the anisotropy index is higher than the preset threshold, the number of spatial directions is increased, such as eight or sixteen directions, and sampling is intensified along the directions with larger variability gradients to capture directional non-uniformity features.

[0087] Regarding similarity scale parameters and attenuation length parameter The standard deviation σ of waveguide parameters within the statistical study area H ,make Where m is an empirical coefficient, for example, ranging from 0.5 to 2; let λ = β·R, where β is an empirical coefficient, for example, ranging from 0.2 to 0.5, and R is the radius of the spatial neighborhood. Through the above adaptive settings, the attenuation rate of the similarity function and distance weight is matched with the actual variation of the waveguide parameters, avoiding the influence of... or Inappropriate values ​​can cause the DSI to be overly sensitive or undersensitive to different regions.

[0088] It should be noted that the above dynamic determination method is only a preferred implementation method. The above parameters can also be fixed based on experience, or the optimal parameter combination can be obtained by training with a small number of samples using the enumeration optimization method.

[0089] S104. Traverse all ocean grid points within the study area to obtain the spatial distribution of waveguide similarity index.

[0090] After calculating the waveguide similarity index for a single target grid point, the above calculation process needs to be extended to all ocean grid points within the entire study area to obtain a spatial distribution map reflecting the horizontal non-uniformity of waveguides across the entire region.

[0091] Specifically, a loop is used to sequentially access each spatial grid point within the study area. For each grid point, it is first determined whether it belongs to an ocean region based on the land-sea mask data described in step S103. If the grid point is a land grid point, it is skipped directly without DSI calculation; if the grid point is an ocean grid point, it is used as the target grid point, and the waveguide similarity index (DSI) value corresponding to the grid point is calculated according to the method described in step S103. The above determination and calculation are repeated until all spatial grid points have been processed.

[0092] After traversal, the DSI values ​​of each ocean grid point are organized according to their geographic coordinates to form a two-dimensional data field corresponding to the original spatial distribution of waveguide parameters, namely the spatial distribution of waveguide similarity index. The value of each grid point in this spatial distribution characterizes the degree of horizontal non-uniformity of waveguide parameters in the local neighborhood at that location.

[0093] S105. Determine the propagation loss deviation corresponding to the study area. The propagation loss deviation is used to characterize the change in propagation loss caused by the horizontal non-uniform waveguide environment relative to the horizontal uniform waveguide environment.

[0094] It should be noted that after obtaining the spatial distribution of the waveguide similarity index, it is necessary to further quantify the influence of the non-uniform waveguide environment on electromagnetic propagation. Specifically, this is achieved by calculating the propagation loss deviation, where propagation loss is a key indicator of electromagnetic wave propagation quality, and a smaller value indicates better propagation conditions. The propagation loss deviation is specifically the average difference between the propagation loss in an actual horizontally non-uniform waveguide environment and the propagation loss in a horizontally uniform waveguide environment constructed using the initial waveguide parameters. This deviation directly reflects the effect of horizontal non-uniformity on the increase or decrease of propagation loss.

[0095] To calculate the propagation loss deviation, it is first necessary to solve for the propagation loss field of the electromagnetic wave in a given waveguide environment. This embodiment uses the parabolic equation (PE) model combined with the split-step Fourier algorithm for numerical solution. The parabolic equation is an efficient approximate model describing the forward propagation of electromagnetic waves in a horizontally non-uniform medium. It can comprehensively consider physical effects such as atmospheric refraction, diffraction, surface reflection, and waveguide trapping, and is a recognized accurate numerical method in the field of electromagnetic propagation.

[0096] Specifically, the energy field distribution of electromagnetic waves is defined as follows: ,in Horizontal propagation distance (unit: km) The vertical height (in meters) is obtained by solving the parabolic equation under given waveguide environment (i.e., atmospheric corrected refractive index profile) and boundary conditions (sea surface impedance). Numerical solution. Propagation loss. (Unit: dB) Free space propagation loss and media propagation loss It consists of two parts, and its calculation formula is as follows:

[0097] ;

[0098] in, The free-space propagation loss caused by the natural diffusion of electromagnetic waves is inversely proportional to the square of the propagation distance; This refers to the additional losses caused by the absorption, scattering, diffraction, and reflection of electromagnetic waves by the propagation medium (atmosphere, sea surface, etc.). The above formula directly converts the numerical solution of the electromagnetic field into the propagation loss value commonly used in engineering.

[0099] To comprehensively evaluate the propagation characteristics along a propagation path, propagation loss bias is defined. In the selected propagation range The average difference in propagation loss between horizontally non-uniform and uniform environments:

[0100] ;

[0101] in, For horizontal propagation distance, The maximum propagation distance is selected (which can be set according to actual application requirements, such as 100km). In actual numerical calculations, the propagation interval can be discretized into multiple step lengths. The absolute values ​​of the propagation loss differences at each step length are summed and then divided by the number of steps to obtain the average deviation along the propagation path, thus reflecting the overall deviation.

[0102] Next, the propagation loss under the two waveguide environments will be calculated, including:

[0103] (1) Calculate the propagation loss in a horizontally non-uniform waveguide environment.

[0104] The actual spatial distribution of waveguide parameters constructed in step S102 is used, where the waveguide parameters at each spatial grid point vary according to their actual values. This propagation occurs along a selected propagation path, for example, starting from a certain initial grid point in the study area and propagating in a specific direction. The propagation loss distribution along this path is obtained by inputting the parabolic equation model and solving it. .

[0105] (2) Calculate the propagation loss in a horizontal uniform waveguide environment constructed using the initial waveguide parameters.

[0106] A hypothetical horizontally homogeneous waveguide environment is constructed, assuming that the waveguide parameters along the entire propagation path are equal to those at the initial grid point (e.g., the evaporation waveguide height remains constant), and that other conditions (such as frequency, antenna height, polarization, sea surface conditions, etc.) are identical to those in the non-homogeneous environment. The propagation loss distribution in this homogeneous environment is then solved using a parabolic equation model. .

[0107] (3) Determine the propagation loss deviation based on the average of the differences between the propagation loss in the horizontally non-uniform waveguide environment and the propagation loss in the horizontally uniform waveguide environment. .

[0108] The magnitude of this deviation reflects the degree to which horizontal non-uniformity affects electromagnetic propagation. The larger the deviation, the greater the increase (or decrease) in propagation loss caused by the non-uniform environment.

[0109] For multiple propagation paths or different starting points within the study area, repeating the above calculations yields the spatial distribution of propagation loss deviation. This can be represented by different color depths. The magnitude of the value indicates that the propagation loss deviation is larger in nearshore areas (darker color) and smaller in offshore areas (lighter color), showing the opposite trend to the waveguide similarity index distribution shown in a certain sea area. That is, the lower the DSI (the more uneven the waveguide), the greater the propagation loss deviation.

[0110] It should be noted that the solution parameters for the parabolic equation in this embodiment, such as frequency, antenna height, polarization, and propagation distance, can be flexibly set according to the actual application scenario. They can be calculated using typical parameters, such as a frequency of 15 GHz and an antenna height of 20 m. Specifically, these parameters can be changed to analyze their impact on propagation loss deviation.

[0111] S106. Establish the correspondence between the waveguide similarity index and the propagation loss deviation.

[0112] After obtaining the waveguide similarity index (DSI) of each ocean grid point in the study area and the propagation loss deviation (ΔL) at each corresponding location, it is also necessary to reveal the inherent correlation between the two.

[0113] Specifically, establishing the correspondence between the waveguide similarity index and the propagation loss deviation includes:

[0114] (1) Statistically analyze the waveguide similarity index and propagation loss deviation corresponding to multiple spatial locations.

[0115] The DSI values ​​and ΔL values ​​calculated in steps S104 and S105 are matched one-to-one according to spatial location to form a statistical data set containing several sample points. Specifically, for each ocean grid point (or multiple randomly selected representative grid points) within the study area, the DSI value at that grid point and the ΔL value calculated when propagating along a specific direction from that grid point are extracted to form a paired sample. To improve statistical reliability, the sample size should cover areas with different spatial characteristics within the study area (e.g., nearshore areas, offshore areas, transitional areas, etc.), typically no less than 30 sample points.

[0116] (2) Establish the correspondence between waveguide similarity index and propagation loss deviation based on statistical results; the correspondence is a negative correlation.

[0117] The above statistical samples were plotted on a two-dimensional coordinate system, with DSI as the abscissa and ΔL as the ordinate, and correlation analysis was performed. Figure 2 This example illustrates the correlation analysis results between the waveguide similarity index and propagation loss deviation. Please refer to... Figure 2 In the graph, each scatter point represents a sample at a spatial location. The horizontal axis represents the DSI value (dimensionless, ranging from 0 to 1), and the vertical axis represents the ΔL value (unit: dB). From Figure 2 As can be seen, there is a clear negative correlation between DSI and ΔL: as DSI increases (the more uniform the waveguide), ΔL gradually decreases; as DSI decreases (the less uniform the waveguide), ΔL gradually increases.

[0118] Further linear regression or curve fitting is performed on the sample data to calculate the Pearson correlation coefficient. In this embodiment, the correlation coefficient is calculated as follows: The result indicates a moderately strong and significant negative correlation between the two. This statistical result verifies that the waveguide similarity index can effectively characterize the influence of horizontal inhomogeneity on propagation loss, and that the correlation is stable and repeatable.

[0119] Based on the above stable correspondence, a rapid assessment model for propagation loss deviation can be established. For example, it can be fitted with... The method employs a linear empirical formula or constructs a mapping relationship between DSI intervals and ΔL ranges. For example, the DSI can be divided into multiple continuous intervals, each corresponding to an empirical ΔL range. In practical engineering applications, for any target spatial location, only the DSI value at that location needs to be calculated according to steps S101 to S104. The corresponding ΔL value can then be quickly estimated using this empirical formula or mapping table, without the need to acquire real-time meteorological data or solve parabolic equations. This significantly reduces data acquisition and computation costs and eliminates the dependence on complete path profile data.

[0120] It should be noted that the correlation coefficients given in this embodiment are... The linear fitting parameters are merely examples based on data from a specific time period in a particular sea area. The specific values ​​of the correlation coefficients may vary in different study areas and under different meteorological conditions, but the negative correlation is universally applicable.

[0121] It should be noted that the negative correlation model established through step S106 (e.g., linear empirical formula) The model is based on the overall fitting results of statistical samples. In practical engineering applications, the waveguide environment of the target sea area may deviate from the historical data used in modeling, and directly using the global model may lead to evaluation errors. Therefore, this embodiment further provides an adaptive correction method.

[0122] First, obtain measured propagation loss data for a small number of locations within the target sea area. This can be achieved through methods such as using an electromagnetic wave propagation tester mounted on a UAV or measuring the actual received signal strength of a shipborne communication system to obtain the true propagation loss values ​​for several discrete points. Then, convert the measured data into measured propagation loss deviations. For each measured point, using the horizontal uniform waveguide environment defined in step S105 as a reference, calculate the difference between the measured propagation loss and the theoretical propagation loss under the uniform environment to obtain the propagation loss at that point. .

[0123] Next, the predicted value for that location based on the current model is calculated. And determine the prediction error y=ΔL pred -ΔL meas Based on the errors at multiple measured points, the least squares method or Kalman filtering is used to adjust the model coefficients. and Perform online updates to obtain the corrected correspondence. .

[0124] Through the above adaptive correction, this method can continuously optimize the evaluation accuracy, adapt to the changes in waveguide environment in different regions and seasons, and significantly improve the generalization ability and engineering applicability of the model.

[0125] S107. Determine the propagation loss deviation corresponding to the target spatial location based on the waveguide similarity index corresponding to the target spatial location and the corresponding relationship.

[0126] Specifically, for any target spatial location to be evaluated, such as a relay point in a maritime communication link, a point within a radar detection area, or the current location of a shipborne communication system, the meteorological data at that location is first acquired according to steps S101 to S104. The spatial distribution of waveguide parameters is then retrieved, and the corresponding waveguide similarity index (DSI) is calculated. When calculating the DSI, the target spatial location is used as the target grid point, and a spatial neighborhood is constructed and calculated according to the method described in step S103.

[0127] After obtaining the DSI value of the target spatial location, the propagation loss deviation ΔL corresponding to that location is determined using the correspondence established in step S106. Specific implementation methods include: directly calculating ΔL by substituting the DSI value into an empirical fitting formula; or obtaining an estimated value of ΔL through interval matching based on a pre-established correspondence between the DSI interval and the ΔL range. Since step S106 has verified the statistical significance and stability of this correspondence, the ΔL value obtained through the above methods has high reliability and does not require re-solving the parabolic equation numerically, significantly reducing computational costs.

[0128] Furthermore, to facilitate engineers' intuitive understanding of the impact of non-uniform waveguide environments on electromagnetic propagation, the waveguide environment level corresponding to the target spatial location can be determined based on the waveguide similarity index (DSI). The electromagnetic propagation impact assessment result is then output based on the propagation loss deviation range corresponding to the waveguide environment level. Specifically, the waveguide environment at the target spatial location is classified into levels based on the DSI value. For example, the DSI value can be divided into multiple intervals: DSI greater than or equal to 0.7 corresponds to a uniform level, DSI greater than or equal to 0.5 and less than 0.7 corresponds to a relatively uniform level, DSI greater than or equal to 0.3 and less than 0.5 corresponds to a relatively non-uniform level, and DSI less than 0.3 corresponds to a highly non-uniform level. Each waveguide environment level can correspond to an empirical range of propagation loss deviation; for example, a uniform level corresponds to ΔL not exceeding 5dB, and a highly non-uniform level corresponds to ΔL not less than 8dB. After determining the level of the target spatial location based on its DSI value, the corresponding electromagnetic propagation impact assessment result can be output, such as good communication quality, potential beyond-line-of-sight propagation attenuation, and engineering suggestions to adjust antenna height or operating frequency.

[0129] It should be noted that the above classification standards and corresponding ΔL ranges are only examples. In actual applications, they can be customized according to the performance requirements of specific communication or radar systems, allowable propagation loss margins, and other factors.

[0130] To further guide the parameter configuration of maritime communication and radar systems, this embodiment analyzes the variation of propagation loss deviation with propagation distance under different electromagnetic wave frequencies and different antenna heights. Figure 3The graph showing the relationship between the calculated propagation loss deviation and propagation distance in this embodiment is provided for reference. Figure 3 , Figure 3 Figure (a) shows the variation of propagation loss deviation with propagation distance under different electromagnetic wave frequencies. Figure 3 As shown in Figure (a), under different frequency conditions, the propagation loss deviation generally increases with the increase of propagation distance, and the increase is more obvious in the long-distance propagation range. As the propagation distance increases, the difference in propagation loss deviation corresponding to different frequencies gradually increases. This law indicates that the frequency of electromagnetic waves affects the degree to which the horizontal non-uniformity of the waveguide affects the electromagnetic propagation loss.

[0131] Figure 3 Figure (b) shows the variation of propagation loss deviation with propagation distance under different antenna heights. Figure 3 As shown in Figure (b), under different antenna heights, the propagation loss deviation generally increases with increasing propagation distance. In the medium-to-long-distance propagation range, the difference in propagation loss deviation corresponding to different antenna heights gradually increases, and the propagation loss deviation corresponding to higher antennas is generally larger. This pattern indicates that antenna height affects the degree to which waveguide horizontal inhomogeneity influences the electromagnetic propagation process.

[0132] based on Figure 3 The calculation results shown indicate that, under the corresponding operating conditions in this embodiment, a higher operating frequency or a lower antenna height corresponds to a smaller propagation loss deviation, which can be used as a reference for configuring communication and radar system parameters.

[0133] The method provided in this embodiment first transforms the spatial distribution characteristics of waveguide parameters into a quantifiable single index by constructing a waveguide similarity index. This index comprehensively reflects the continuity and drastic changes of waveguide parameters in different spatial directions within the neighborhood of the target grid point, providing a unified quantitative basis for subsequent evaluation. Secondly, by calculating the propagation loss deviation and establishing its negative correlation with the waveguide similarity index, the physical law of the influence of waveguide horizontal inhomogeneity on electromagnetic propagation is revealed: the larger the DSI (the more uniform the waveguide), the smaller the propagation loss deviation, and vice versa. Based on this stable correspondence, this method only needs to calculate the waveguide similarity index at the target spatial location to quickly obtain the corresponding propagation loss deviation, without needing to solve the parabolic equation point by point along the propagation path, thus significantly reducing computational costs and eliminating dependence on complete path profile data. Furthermore, this embodiment also provides two preferred alternative implementation methods: one is to dynamically determine the DSI calculation parameters based on the spatial variation characteristics of waveguide parameters, so that the method can adapt to the changes in waveguide environment in different sea areas and seasons; the other is to use a small amount of measured propagation loss data to perform online correction on the DSI-ΔL correspondence model and continuously optimize the evaluation accuracy. These designs significantly improve the generalization ability and engineering practicality of the method.

[0134] Example 2

[0135] Corresponding to the aforementioned embodiment of a method for quantitatively assessing the impact of electromagnetic propagation in a non-uniform atmospheric waveguide environment, this application also provides an embodiment of a device for quantitatively assessing the impact of electromagnetic propagation in a non-uniform atmospheric waveguide environment.

[0136] Figure 4 This is a schematic diagram of the second embodiment of the device for quantifying the impact of electromagnetic propagation in a non-uniform atmospheric waveguide environment provided in this application. Please refer to... Figure 4 The apparatus provided in this embodiment includes an acquisition module 410, a construction module 420, a calculation module 430, a traversal module 440, a determination module 450, and an establishment module 460. The acquisition module 410 is used to acquire meteorological data of the study area; The construction module 420 is used to invert the meteorological data to obtain the waveguide parameters corresponding to each spatial grid point in the study area, and to construct the spatial distribution of waveguide parameters. The calculation module 430 is used to construct a spatial neighborhood centered on the target grid point in the ocean area, obtain waveguide parameters in the spatial neighborhood along multiple spatial directions, and calculate the waveguide similarity index corresponding to the target grid point based on the difference between the waveguide parameters in different spatial directions and the waveguide parameters of the target grid point, as well as the spatial distance between the target grid point and the sampling points in the neighborhood. The traversal module 440 is used to traverse each ocean grid point in the study area to obtain the spatial distribution of waveguide similarity index. The determining module 450 is used to determine the propagation loss deviation corresponding to the study area. The propagation loss deviation is used to characterize the change in propagation loss caused by the horizontal non-uniform waveguide environment relative to the horizontal uniform waveguide environment. The establishment module 460 is used to establish the correspondence between the waveguide similarity index and the propagation loss deviation; The determining module 450 is further configured to determine the propagation loss deviation corresponding to the target spatial location based on the waveguide similarity index corresponding to the target spatial location and the corresponding relationship.

[0137] The apparatus of this embodiment can be used to perform... Figure 1 The steps of the method embodiment shown are similar in principle and process, and will not be repeated here.

[0138] The specific implementation process of the functions and roles of each unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0139] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0140] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for quantitatively assessing the impact of electromagnetic propagation in a non-uniform atmospheric waveguide environment, characterized in that, The method includes: Obtain meteorological data for the study area; The waveguide parameters corresponding to each spatial grid point within the study area are obtained by inversion based on the meteorological data, and the spatial distribution of waveguide parameters is constructed. The process of obtaining the waveguide parameters corresponding to each spatial grid point within the study area by inversion based on the meteorological data includes: constructing an evaporation waveguide diagnostic model based on the Monin-Obukhov similarity theory; processing the meteorological data using the evaporation waveguide diagnostic model to obtain the evaporation waveguide height corresponding to each spatial grid point; and using the evaporation waveguide height as the waveguide parameter of the corresponding spatial grid point. For a target grid point in an ocean region, a spatial neighborhood is constructed centered on the target grid point. Waveguide parameters within the spatial neighborhood are acquired along multiple spatial directions. Based on the differences between the waveguide parameters in different spatial directions and the waveguide parameters of the target grid point, as well as the spatial distance between the target grid point and the neighboring sampling points, a waveguide similarity index corresponding to the target grid point is calculated. The calculation of the waveguide similarity index based on the differences between the waveguide parameters in different spatial directions and the waveguide parameters of the target grid point includes: for each spatial direction, calculating the parameter difference between the waveguide parameters corresponding to each neighboring sampling point in the current spatial direction and the waveguide parameters corresponding to the target grid point; and determining the corresponding neighboring sampling points based on each parameter difference. The similarity value of the points is used to calculate the directional similarity index of the corresponding spatial direction; the calculation of the waveguide similarity index based on the spatial distance between the target grid point and the neighboring sampling points includes: for each spatial direction, calculating the spatial distance between each neighboring sampling point and the target grid point in the current spatial direction; determining the corresponding distance weight according to the spatial distance between each neighboring sampling point and the target grid point; using the distance weight to weight the similarity values ​​corresponding to each neighboring sampling point in the current spatial direction to obtain the directional similarity index corresponding to the current spatial direction; averaging the directional similarity indices corresponding to multiple spatial directions to obtain the waveguide similarity index corresponding to the target grid point; By traversing all ocean grid points within the study area, the spatial distribution of waveguide similarity index was obtained; Determine the propagation loss deviation corresponding to the study area. The propagation loss deviation is used to characterize the change in propagation loss caused by the horizontal non-uniform waveguide environment relative to the horizontal uniform waveguide environment. Establish the correspondence between the waveguide similarity index and the propagation loss deviation; Based on the waveguide similarity index corresponding to the target spatial location and the corresponding relationship, the propagation loss deviation corresponding to the target spatial location is determined.

2. The method according to claim 1, characterized in that, The meteorological data includes temperature data, relative humidity data, air pressure data, sea surface temperature data, and wind speed data.

3. The method according to claim 1, characterized in that, Before calculating the waveguide similarity index corresponding to the target grid point, the following steps are included: Obtain land-sea mask data corresponding to the study area; Identify spatial grid points corresponding to the marine region based on the aforementioned land-sea mask data; The identified marine regional spatial grid points are used as the objects for waveguide similarity index calculation.

4. The method according to claim 1, characterized in that, The step of constructing a spatial neighborhood centered on the target grid point and obtaining waveguide parameters within the spatial neighborhood along multiple spatial directions includes: A spatial neighborhood of a preset range is established with the target grid point as the center; Obtain neighborhood sampling points within the spatial neighborhood along multiple preset spatial directions; Obtain the waveguide parameters corresponding to each neighborhood sampling point.

5. The method according to claim 1, characterized in that, The determination of the propagation loss deviation corresponding to the study area includes: Calculate the propagation loss in a horizontally non-uniform waveguide environment; Calculate the propagation loss in a horizontally uniform waveguide environment constructed using the initial waveguide parameters; The propagation loss deviation is determined by averaging the difference between the propagation loss in the horizontally non-uniform waveguide environment and the propagation loss in the horizontally uniform waveguide environment.

6. The method according to claim 1, characterized in that, Establishing the correspondence between the waveguide similarity index and the propagation loss deviation includes: Statistical analysis of waveguide similarity index and propagation loss deviation at multiple spatial locations; A correlation between waveguide similarity index and propagation loss deviation is established based on statistical results; the correlation is negative.

7. A device for quantitatively evaluating the impact of electromagnetic propagation in a non-uniform atmospheric waveguide environment, characterized in that, The device includes an acquisition module, a construction module, a calculation module, a traversal module, a determination module, and an establishment module; The acquisition module is used to acquire meteorological data of the study area; The construction module is used to invert the waveguide parameters corresponding to each spatial grid point within the study area based on the meteorological data, and to construct the spatial distribution of waveguide parameters. The step of inverting the waveguide parameters corresponding to each spatial grid point within the study area based on the meteorological data includes: constructing an evaporation waveguide diagnostic model based on the Monin-Obukhov similarity theory; processing the meteorological data using the evaporation waveguide diagnostic model to obtain the evaporation waveguide height corresponding to each spatial grid point; and using the evaporation waveguide height as the waveguide parameter of the corresponding spatial grid point. The calculation module is used to construct a spatial neighborhood centered on a target grid point in an ocean region, acquire waveguide parameters within the spatial neighborhood along multiple spatial directions, and calculate the waveguide similarity index corresponding to the target grid point based on the differences between the waveguide parameters in different spatial directions and the waveguide parameters of the target grid point, as well as the spatial distance between the target grid point and the neighboring sampling points. The calculation of the waveguide similarity index based on the differences between the waveguide parameters in different spatial directions and the waveguide parameters of the target grid point includes: for each spatial direction, calculating the parameter difference between the waveguide parameters corresponding to each neighboring sampling point in the current spatial direction and the waveguide parameters corresponding to the target grid point; and determining the similarity index based on each parameter difference. The similarity value of the neighboring sampling points is used to calculate the directional similarity index of the corresponding spatial direction. The calculation of the waveguide similarity index based on the spatial distance between the target grid point and the neighboring sampling points includes: for each spatial direction, calculating the spatial distance between each neighboring sampling point and the target grid point in the current spatial direction; determining the corresponding distance weight based on the spatial distance between each neighboring sampling point and the target grid point; using the distance weight to weight the similarity values ​​corresponding to each neighboring sampling point in the current spatial direction to obtain the directional similarity index corresponding to the current spatial direction; and averaging the directional similarity indices corresponding to multiple spatial directions to obtain the waveguide similarity index corresponding to the target grid point. The traversal module is used to traverse each ocean grid point within the study area to obtain the spatial distribution of the waveguide similarity index; The determining module is used to determine the propagation loss deviation corresponding to the study area. The propagation loss deviation is used to characterize the change in propagation loss caused by the horizontal non-uniform waveguide environment relative to the horizontal uniform waveguide environment. The establishment module is used to establish the correspondence between the waveguide similarity index and the propagation loss deviation; The determining module is further configured to determine the propagation loss deviation corresponding to the target spatial location based on the waveguide similarity index corresponding to the target spatial location and the corresponding relationship.

Images